Browsing by Author "Onan A."
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Item Multicenter analysis of gestational trophoblastic neoplasia in Turkey(Asian Pacific Organization for Cancer Prevention, 2014) Ozalp S.S.; Telli E.; Oge T.; Tulunay G.; Boran N.; Turan T.; Yenen M.; Kurdoglu Z.; Ozler A.; Yuce K.; Ulker V.; Arvas M.; Demirkiran F.; Bese T.; Tokgozoglu N.; Onan A.; Sanci M.; Gokcu M.; Tosun G.; Dikmen Y.; Ozsaran A.; Terek M.C.; Akman L.; Yetimalar H.; Kilic D.S.; Gungor T.; Ozgu E.; Yildiz Y.; Kokcu A.; Kefeli M.; Kuruoglu S.; Yuksel H.; Guvenal T.; Hasdemir P.S.; Ozcelik B.; Serin S.; Dolanbay M.; Arioz D.T.; Tuncer N.; Bozkaya H.; Guven S.; Kulaksiz D.; Varol F.; Yanik A.; Ogurlu G.; Simsek T.; Toptas T.; Dogan S.; Camuzoglu H.; Api M.; Guzin K.; Caliskan E.; Doger E.; Cakmak B.; Ilhan T.T.Background: To evaluate the incidence, diagnosis and management of GTN among 28 centers in Turkey. Materials and Methods: A retrospective study was designed to include GTN patients attending 28 centers in the 10-year period between January 2003 and May 2013. Demographical characteristics of the patients, histopathological diagnosis, the International Federation of Gynecology and Obstetrics (FIGO) anatomical and prognostic scores, use of single-agent and multi-agent chemotherapy, surgical interventions and prognosis were evaluated. Results: From 2003-2013, there were 1,173,235 deliveries and 456 GTN cases at the 28 centers. The incidence was calculated to be 0.38 per 1,000 deliveries. According to the evaluated data of 364 patients, the median age at diagnosis was 31 years (range, 15-59 years). A histopathological diagnosis was present for 45.1% of the patients, and invasive mole, choriocarcinoma and PSTTs were diagnosed in 22.3% (n=81), 18.1% (n=66) and 4.7% (n=17) of the patients, respectively. Regarding final prognosis, 352 (96.7%) of the patients had remission, and 7 (1.9%) had persistence, whereas the disease was mortal for 5 (1.4%) of the patients. Conclusions: Because of the differences between countries, it is important to provide national registration systems and special clinics for the accurate diagnosis and treatment of GTN.Item A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer(Elsevier Ltd, 2015) Onan A.Breast cancer is one of the most common and deadly cancer for women. Early diagnosis and treatment of breast cancer can enhance the outcome of the patients. The development of classification models with high accuracy is an essential task in medical informatics. Machine learning algorithms have been widely employed to build robust and efficient classification models. In this paper, we present a hybrid intelligent classification model for breast cancer diagnosis. The proposed classification model consists of three phases: instance selection, feature selection and classification. In instance selection, the fuzzy-rough instance selection method based on weak gamma evaluator is utilized to remove useless or erroneous instances. In feature selection, the consistency-based feature selection method is used in conjunction with a re-ranking algorithm, owing to its efficiency in searching the possible enumerations in the search space. In the classification phase of the model, the fuzzy-rough nearest neighbor algorithm is utilized. Since this classifier does not require the optimal value for K neighbors and has richer class confidence values, this approach is utilized for the classification task. To test the efficacy of the proposed classification model we used the Wisconsin Breast Cancer Dataset (WBCD). The performance is evaluated using classification accuracy, sensitivity, specificity, F-measure, area under curve, and Kappa statistics. The obtained classification accuracy of 99.7151% is a very promising result compared to the existing works in this area reporting the results for the same data set. © 2015 Elsevier Ltd. All rights reserved.Item On the performance of ensemble learning for automated diagnosis of breast cancer(Springer Verlag, 2015) Onan A.The automated diagnosis of diseases with high accuracy rate is one of the most crucial problems in medical informatics. Machine learning algorithms are widely utilized for automatic detection of illnesses. Breast cancer is one of the most common cancer types in females and the second most common cause of death from cancer in females. Hence, developing an efficient classifier for automated diagnosis of breast cancer is essential to improve the chance of diagnosing the disease at the earlier stages and treating it more properly. Ensemble learning is a branch of machine learning that seeks to use multiple learning algorithms so that better predictive performance acquired. Ensemble learning is a promising field for improving the performance of base classifiers. This paper is concerned with the comparative assessment of the performance of six popular ensemble methods (Bagging, Dagging, Ada Boost, Multi Boost, Decorate, and Random Subspace) based on fourteen base learners (Bayes Net, FURIA, Knearest Neighbors, C4.5, RIPPER, Kernel Logistic Regression, K-star, Logistic Regression, Multilayer Perceptron, Naïve Bayes, Random Forest, Simple Cart, Support Vector Machine, and LMT) for automatic detection of breast cancer. The empirical results indicate that ensemble learning can improve the predictive performance of base learners on medical domain. The best results for comparative experiments are acquired with Random Subspace ensemble method. The experiments show that ensemble learning methods are appropriate methods to improve the performance of classifiers for medical diagnosis. © Springer International Publishing Switzerland 2015.Item Artificial immune system based Web page classification(Springer Verlag, 2015) Onan A.Automated classification of web pages is an important research direction in web mining, which aims to construct a classification model that can classify new instances based on labeled web documents. Machine learning algorithms are adapted to textual classification problems, including web document classification. Artificial immune systems are a branch of computational intelligence inspired by biological immune systems which is utilized to solve a variety of computational problems, including classification. This paper examines the effectiveness and suitability of artificial immune system based approaches for web page classification. Hence, two artificial immune system based classification algorithms, namely Immunos-1 and Immunos-99 algorithms are compared to two standard machine learning techniques, namely C4.5 decision tree classifier and Naïve Bayes classification. The algorithms are experimentally evaluated on 50 data sets obtained from DMOZ (Open Directory Project). The experimental results indicate that artificial immune based systems achieve higher predictive performance for web page classification. © Springer International Publishing Switzerland 2015.Item A stochastic gradient descent based SVM with fuzzy-rough feature selection and instance selection for breast cancer diagnosis(American Scientific Publishers, 2015) Onan A.Breast cancer remains to be one of the most severe and deadly diseases among women in the world. Fortunately, a long survival rate for patients with not metastasized breast cancer can be achieved with the help of early detection, proper treatment and therapy. This urges the need to develop efficient classification models with high predictive performance. Machine learning and artificial intelligence based methods are effectively utilized for building classification models in medical domain. In this paper, fuzzy-rough feature selection based support vector machine classifier with stochastic gradient descent learning is proposed for breast cancer diagnosis. In the proposed model, fuzzy-rough feature selection with particle swarm optimization based search is used for obtaining a subset of relevant features for model. In order to select appropriate instances, a fuzzy-rough instance selection method is utilized. The effectiveness of the proposed classification approach is evaluated on Wisconsin Breast Cancer Dataset (WBCD) with classification evaluation metrics, such as classification accuracy, sensitivity, specificity, F-measure and kappa statistics. Experimental results indicate that the proposed model can achieve a very high predictive performance. Copyright © 2015 American Scientific Publishers.Item Ensemble methods for opinion mining; [Görüş Madenciliʇinde Siniflandirici Topluluklari](Institute of Electrical and Electronics Engineers Inc., 2015) Onan A.; Korukoglu S.Opinion mining is an emerging field which uses computer science methods to extract subjective information, such as opinion, emotion, and attitude inherent in opinion holder's text. One of the major issues in opinion mining is to enhance the predictive performance of classification algorithm. Ensemble methods used for opinion mining aim to obtain robust classification models by combining decisions obtained by multiple classifier training, rather than depending on a single classifier. In this study, the comparative performance of opinion mining datasets on Bagging, Dagging, Random Subspace and Adaboost ensemble methods with five different classifiers and six different data representation schemes are presented. The experimental results indicate that ensemble methods can be used for building efficient opinion mining classification methods. © 2015 IEEE.Item Exploring performance of instance selection methods in text sentiment classification(Springer Verlag, 2016) Onan A.; Korukoğlu S.Sentiment analysis is the process of extracting subjective information in source materials. Sentiment analysis is a subfield of web and text mining. One major problem encountered in these areas is overwhelming amount of data available. Hence, instance selection and feature selection become two essential tasks for achieving scalability in machine learning based sentiment classification. Instance selection is a data reduction technique which aims to eliminate redundant, noisy data from the training dataset so that training time can be reduced, scalability and generalization ability can be enhanced. This paper examines the predictive performance of fifteen benchmark instance selection methods for text classification domain. The instance selection methods are evaluated by decision tree classifier (C4.5 algorithm) and radial basis function networks in terms of classification accuracy and data reduction rates. The experimental results indicate that the highest classification accuracies on C4.5 algorithm are generally obtained by model class selection method, while the highest classification accuracies on radial basis function networks are obtained by nearest centroid neighbor edition. © Springer International Publishing Switzerland 2016.Item The use of data mining for strategic management: A case study on mining association rules in student information system; [Upotreba rudarenja podataka u strateškom menadžmentu: Analiza slučaja upotrebe pravila pridruživanja rudarenja podataka u informacijskom sustavu podataka o studentima](FACTEACHEREDUCATION, 2016) Onan A.; Bal V.; Bayam B.Y.In today’s competitive conditions changes in business environment and business structures make strategic management an effective form of management for business and organizations. Strategic management is a current management strategy that requires setting of the appropriate strategies, plans and applications and putting them into action in order to reach the aims and goals of organizations. The process of strategic management involves setting the company’s vision, mission and objectives, determining the competitive position, and the evaluation of results obtained by strategy selection, development and application. In the application of activities related to the strategic management of business processes, the discipline of data mining, which can be defined as the process of extracting useful and meaningful patterns from large volumes of data, emerges as a viable method. In this study, strategic management and data mining disciplines and their basic concepts and applications are introduced. Apart from that, data mining methods in the context of strategic management are taken into consideration. In addition, a sample case study about the use of association rule mining algorithms in student information systems data will be presented. © 2016, FACTEACHEREDUCATION. All rights reserved.Item Ensemble of keyword extraction methods and classifiers in text classification(Elsevier Ltd, 2016) Onan A.; Korukoǧlu S.; Bulut H.Automatic keyword extraction is an important research direction in text mining, natural language processing and information retrieval. Keyword extraction enables us to represent text documents in a condensed way. The compact representation of documents can be helpful in several applications, such as automatic indexing, automatic summarization, automatic classification, clustering and filtering. For instance, text classification is a domain with high dimensional feature space challenge. Hence, extracting the most important/relevant words about the content of the document and using these keywords as the features can be extremely useful. In this regard, this study examines the predictive performance of five statistical keyword extraction methods (most frequent measure based keyword extraction, term frequency-inverse sentence frequency based keyword extraction, co-occurrence statistical information based keyword extraction, eccentricity-based keyword extraction and TextRank algorithm) on classification algorithms and ensemble methods for scientific text document classification (categorization). In the study, a comprehensive study of comparing base learning algorithms (Naïve Bayes, support vector machines, logistic regression and Random Forest) with five widely utilized ensemble methods (AdaBoost, Bagging, Dagging, Random Subspace and Majority Voting) is conducted. To the best of our knowledge, this is the first empirical analysis, which evaluates the effectiveness of statistical keyword extraction methods in conjunction with ensemble learning algorithms. The classification schemes are compared in terms of classification accuracy, F-measure and area under curve values. To validate the empirical analysis, two-way ANOVA test is employed. The experimental analysis indicates that Bagging ensemble of Random Forest with the most-frequent based keyword extraction method yields promising results for text classification. For ACM document collection, the highest average predictive performance (93.80%) is obtained with the utilization of the most frequent based keyword extraction method with Bagging ensemble of Random Forest algorithm. In general, Bagging and Random Subspace ensembles of Random Forest yield promising results. The empirical analysis indicates that the utilization of keyword-based representation of text documents in conjunction with ensemble learning can enhance the predictive performance and scalability of text classification schemes, which is of practical importance in the application fields of text classification. © 2016 Elsevier Ltd. All rights reserved.Item A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification(Elsevier Ltd, 2016) Onan A.; Korukoğlu S.; Bulut H.Typically performed by supervised machine learning algorithms, sentiment analysis is highly useful for extracting subjective information from text documents online. Most approaches that use ensemble learning paradigms toward sentiment analysis involve feature engineering in order to enhance the predictive performance. In response, we sought to develop a paradigm of a multiobjective, optimization-based weighted voting scheme to assign appropriate weight values to classifiers and each output class based on the predictive performance of classification algorithms, all to enhance the predictive performance of sentiment classification. The proposed ensemble method is based on static classifier selection involving majority voting error and forward search, as well as a multiobjective differential evolution algorithm. Based on the static classifier selection scheme, our proposed ensemble method incorporates Bayesian logistic regression, naïve Bayes, linear discriminant analysis, logistic regression, and support vector machines as base learners, whose performance in terms of precision and recall values determines weight adjustment. Our experimental analysis of classification tasks, including sentiment analysis, software defect prediction, credit risk modeling, spam filtering, and semantic mapping, suggests that the proposed classification scheme can predict better than conventional ensemble learning methods such as AdaBoost, bagging, random subspace, and majority voting. Of all datasets examined, the laptop dataset showed the best classification accuracy (98.86%). © 2016 Elsevier LtdItem Classifier and feature set ensembles for web page classification(SAGE Publications Ltd, 2016) Onan A.Web page classification is an important research direction on web mining. The abundant amount of data available on the web makes it essential to develop efficient and robust models for web mining tasks. Web page classification is the process of assigning a web page to a particular predefined category based on labelled data. It serves for several other web mining tasks, such as focused web crawling, web link analysis and contextual advertising. Machine learning and data mining methods have been successfully applied for several web mining tasks, including web page classification. Multiple classifier systems are a promising research direction in machine learning, which aims to combine several classifiers by differentiating base classifiers and/or dataset distributions so that more robust classification models can be built. This paper presents a comparative analysis of four different feature selections (correlation, consistency, information gain and chi-square-based feature selection) and four different ensemble learning methods (Boosting, Bagging, Dagging and Random Subspace) based on four different base learners (naive Bayes, K-nearest neighbour algorithm, C4.5 algorithm and FURIA algorithm). The article examines the predictive performance of ensemble methods for web page classification. The experimental results indicate that feature selection and ensemble learning can enhance the predictive performance of classifiers in web page classification. For the DMOZ-50 dataset, the highest average predictive performance (88.1%) is obtained with the combination of consistency-based feature selection with AdaBoost and naive Bayes algorithms, which is a promising result for web page classification. Experimental results indicate that Bagging and Random Subspace ensemble methods and correlation-based and consistency-based feature selection methods obtain better results in terms of accuracy rates. © Chartered Institute of Library and Information Professionals.Item An improved ant algorithm with LDA-based representation for text document clustering(SAGE Publications Ltd, 2017) Onan A.; Bulut H.; Korukoglu S.Document clustering can be applied in document organisation and browsing, document summarisation and classification. The identification of an appropriate representation for textual documents is extremely important for the performance of clustering or classification algorithms. Textual documents suffer from the high dimensionality and irrelevancy of text features. Besides, conventional clustering algorithms suffer from several shortcomings, such as slow convergence and sensitivity to the initial value. To tackle the problems of conventional clustering algorithms, metaheuristic algorithms are frequently applied to clustering. In this paper, an improved ant clustering algorithm is presented, where two novel heuristic methods are proposed to enhance the clustering quality of ant-based clustering. In addition, the latent Dirichlet allocation (LDA) is used to represent textual documents in a compact and efficient way. The clustering quality of the proposed ant clustering algorithm is compared to the conventional clustering algorithms using 25 text benchmarks in terms of F-measure values. The experimental results indicate that the proposed clustering scheme outperforms the compared conventional and metaheuristic clustering methods for textual documents. © Chartered Institute of Library and Information Professionals.Item A feature selection model based on genetic rank aggregation for text sentiment classification(SAGE Publications Ltd, 2017) Onan A.; KorukoGlu S.Sentiment analysis is an important research direction of natural language processing, text mining and web mining which aims to extract subjective information in source materials. The main challenge encountered in machine learning method-based sentiment classification is the abundant amount of data available. This amount makes it difficult to train the learning algorithms in a feasible time and degrades the classification accuracy of the built model. Hence, feature selection becomes an essential task in developing robust and efficient classification models whilst reducing the training time. In text mining applications, individual filter-based feature selection methods have been widely utilized owing to their simplicity and relatively high performance. This paper presents an ensemble approach for feature selection, which aggregates the several individual feature lists obtained by the different feature selection methods so that a more robust and efficient feature subset can be obtained. In order to aggregate the individual feature lists, a genetic algorithm has been utilized. Experimental evaluations indicated that the proposed aggregation model is an efficient method and it outperforms individual filter-based feature selection methods on sentiment classification. © The Author(s) 2015.Item A machine learning based approach to identify geo-location of Twitter users(Association for Computing Machinery, 2017) Onan A.Twitter, a popular microblogging platform, has attracted great attention. Twitter enables people from all over the world to interact in an extremely personal way. The immense quantity of user-generated text messages become available on Twitter that could potentially serve as an important source of information for researchers and practitioners. The information available on Twitter may be utilized for many purposes, such as event detection, public health and crisis management. In order to effectively coordinate such activities, the identification of Twitter users' geo-locations is extremely important. Though online social networks can provide some sort of geo-location information based on GPS coordinates, Twitter suffers from geo-location sparseness problem. The identification of Twitter users' geo-location based on the content of send out messages, becomes extremely important. In this regard, this paper presents a machine learning based approach to the problem. In this study, our corpora is represented as a word vector. To obtain a classification scheme with high predictive performance, the performance of five classification algorithms, three ensemble methods and two feature selection methods are evaluated. Among the compared algorithms, the highest results (84.85%) is achieved by AdaBoost ensemble of Random Forest, when the feature set is selected with the use of consistency-based feature selection method in conjunction with best first search. © 2017 ACM.Item Hybrid supervised clustering based ensemble scheme for text classification(Emerald Group Publishing Ltd., 2017) Onan A.Purpose: The immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in information retrieval, such as document organization, text filtering and sentiment analysis. Ensemble learning has been extensively studied to construct efficient text classification schemes with higher predictive performance and generalization ability. The purpose of this paper is to provide diversity among the classification algorithms of ensemble, which is a key issue in the ensemble design. Design/methodology/approach: An ensemble scheme based on hybrid supervised clustering is presented for text classification. In the presented scheme, supervised hybrid clustering, which is based on cuckoo search algorithm and k-means, is introduced to partition the data samples of each class into clusters so that training subsets with higher diversities can be provided. Each classifier is trained on the diversified training subsets and the predictions of individual classifiers are combined by the majority voting rule. The predictive performance of the proposed classifier ensemble is compared to conventional classification algorithms (such as Naïve Bayes, logistic regression, support vector machines and C4.5 algorithm) and ensemble learning methods (such as AdaBoost, bagging and random subspace) using 11 text benchmarks. Findings: The experimental results indicate that the presented classifier ensemble outperforms the conventional classification algorithms and ensemble learning methods for text classification. Originality/value: The presented ensemble scheme is the first to use supervised clustering to obtain diverse ensemble for text classification © 2017, © Emerald Publishing Limited.Item Sarcasm identification on Twitter: A machine learning approach(Springer Verlag, 2017) Onan A.In recent years, the remarkable growth in social media and microblogging platforms provide an essential source of information to identify subjective information of people, such as opinions, sentiments and attitudes. Sentiment analysis is the process of identifying subjective information from source materials towards an entity. Much of the social content online contain nonliteral language, such as irony and sarcasm, which may degrade the performance of sentiment classification schemes. In sarcastic text, the expressed text utterances and the intention of the person employing sarcasm can be completely opposite. In this paper, we present a machine learning approach to sarcasm identification. In this scheme, we utilized lexical, pragmatic, dictionary based and part of speech features. We employed two kinds of features to describe lexical information: unigrams and bigrams. In addition, term-frequency, term-presence and TF-IDF based representations are evaluated. To evaluate predictive performance of different representation schemes, Naïve Bayes, support vector machines, logistic regression and k-nearest neighbor classifiers are utilized. © Springer International Publishing AG 2017.Item A K-medoids based clustering scheme with an application to document clustering(Institute of Electrical and Electronics Engineers Inc., 2017) Onan A.Clustering is an important unsupervised data analysis technique, which divides data objects into clusters based on similarity. Clustering has been studied and applied in many different fields, including pattern recognition, data mining, decision science and statistics. Clustering algorithms can be mainly classified as hierarchical and partitional clustering approaches. Partitioning around medoids (PAM) is a partitional clustering algorithms, which is less sensitive to outliers, but greatly affected by the poor initialization of medoids. In this paper, we augment the randomized seeding technique to overcome problem of poor initialization of medoids in PAM algorithm. The proposed approach (PAM++) is compared with other partitional clustering algorithms, such as K-means and K-means++ on text document clustering benchmarks and evaluated in terms of F-measure. The results for experiments indicate that the randomized seeding can improve the performance of PAM algorithm on text document clustering. © 2017 IEEE.Item A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification(Elsevier Ltd, 2017) Onan A.; Korukoğlu S.; Bulut H.Sentiment analysis is a critical task of extracting subjective information from online text documents. Ensemble learning can be employed to obtain more robust classification schemes. However, most approaches in the field incorporated feature engineering to build efficient sentiment classifiers. The purpose of our research is to establish an effective sentiment classification scheme by pursuing the paradigm of ensemble pruning. Ensemble pruning is a crucial method to build classifier ensembles with high predictive accuracy and efficiency. Previous studies employed exponential search, randomized search, sequential search, ranking based pruning and clustering based pruning. However, there are tradeoffs in selecting the ensemble pruning methods. In this regard, hybrid ensemble pruning schemes can be more promising. In this study, we propose a hybrid ensemble pruning scheme based on clustering and randomized search for text sentiment classification. Furthermore, a consensus clustering scheme is presented to deal with the instability of clustering results. The classifiers of the ensemble are initially clustered into groups according to their predictive characteristics. Then, two classifiers from each cluster are selected as candidate classifiers based on their pairwise diversity. The search space of candidate classifiers is explored by the elitist Pareto-based multi-objective evolutionary algorithm. For the evaluation task, the proposed scheme is tested on twelve balanced and unbalanced benchmark text classification tasks. In addition, the proposed approach is experimentally compared with three ensemble methods (AdaBoost, Bagging and Random Subspace) and three ensemble pruning algorithms (ensemble selection from libraries of models, Bagging ensemble selection and LibD3C algorithm). Results demonstrate that the consensus clustering and the elitist pareto-based multi-objective evolutionary algorithm can be effectively used in ensemble pruning. The experimental analysis with conventional ensemble methods and pruning algorithms indicates the validity and effectiveness of the proposed scheme. © 2017 Elsevier LtdItem Evidence of associations between brain-derived neurotrophic factor (BDNF) serum levels and gene polymorphisms with tinnitus(Medknow Publications, 2017) Coskunoglu A.; Orenay-Boyacioglu S.; Deveci A.; Bayam M.; Onur E.; Onan A.; Cam F.S.Background: Brain-derived neurotrophic factor (BDNF) gene polymorphisms are associated with abnormalities in regulation of BDNF secretion. Studies also linked BDNF polymorphisms with changes in brainstem auditory-evoked response test results. Furthermore, BDNF levels are reduced in tinnitus, psychiatric disorders, depression, dysthymic disorder that may be associated with stress, conversion disorder, and suicide attempts due to crises of life. For this purpose, we investigated whether there is any role of BDNF changes in the pathophysiology of tinnitus. Materials and Methods: In this study, we examined the possible effects of BDNF variants in individuals diagnosed with tinnitus for more than 3 months. Fifty-two tinnitus subjects between the ages of 18 and 55, and 42 years healthy control subjects in the same age group, who were free of any otorhinolaryngology and systemic disease, were selected for examination. The intensity of tinnitus and depression was measured using the tinnitus handicap inventory, and the differential diagnosis of psychiatric diagnoses made using the Structured Clinical Interview for Fourth Edition of Mental Disorders. BDNF gene polymorphism was analyzed in the genomic deoxyribonucleic acid (DNA) samples extracted from the venous blood, and the serum levels of BDNF were measured. One-way analysis of variance and Chi-squared tests were applied. Results: Serum BDNF level was found lower in the tinnitus patients than controls, and it appeared that there is no correlation between BDNF gene polymorphism and tinnitus. Conclusions: This study suggests neurotrophic factors such as BDNF may have a role in tinnitus etiology. Future studies with larger sample size may be required to further confirm our results. © 2017 Noise & Health | Published by Wolters Kluwer - Medknow.Item Biomedical Text Categorization Based on Ensemble Pruning and Optimized Topic Modelling(Hindawi Limited, 2018) Onan A.Text mining is an important research direction, which involves several fields, such as information retrieval, information extraction, and text categorization. In this paper, we propose an efficient multiple classifier approach to text categorization based on swarm-optimized topic modelling. The Latent Dirichlet allocation (LDA) can overcome the high dimensionality problem of vector space model, but identifying appropriate parameter values is critical to performance of LDA. Swarm-optimized approach estimates the parameters of LDA, including the number of topics and all the other parameters involved in LDA. The hybrid ensemble pruning approach based on combined diversity measures and clustering aims to obtain a multiple classifier system with high predictive performance and better diversity. In this scheme, four different diversity measures (namely, disagreement measure, Q-statistics, the correlation coefficient, and the double fault measure) among classifiers of the ensemble are combined. Based on the combined diversity matrix, a swarm intelligence based clustering algorithm is employed to partition the classifiers into a number of disjoint groups and one classifier (with the highest predictive performance) from each cluster is selected to build the final multiple classifier system. The experimental results based on five biomedical text benchmarks have been conducted. In the swarm-optimized LDA, different metaheuristic algorithms (such as genetic algorithms, particle swarm optimization, firefly algorithm, cuckoo search algorithm, and bat algorithm) are considered. In the ensemble pruning, five metaheuristic clustering algorithms are evaluated. The experimental results on biomedical text benchmarks indicate that swarm-optimized LDA yields better predictive performance compared to the conventional LDA. In addition, the proposed multiple classifier system outperforms the conventional classification algorithms, ensemble learning, and ensemble pruning methods. © 2018 Aytuǧ Onan.