A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification
• A novel multi-objective differential evolution algorithm based classifier ensemble for text sentiment classification.• An empirical comparison of weighted and unweighted voting schemes.• Extensive empirical analysis on metaheuristic based voting schemes for sentiment analysis.• High classification...
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Veröffentlicht in: | Expert systems with applications 2016-11, Vol.62, p.1-16 |
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creator | Onan, Aytuğ Korukoğlu, Serdar Bulut, Hasan |
description | • A novel multi-objective differential evolution algorithm based classifier ensemble for text sentiment classification.• An empirical comparison of weighted and unweighted voting schemes.• Extensive empirical analysis on metaheuristic based voting schemes for sentiment analysis.• High classification accuracies for text sentiment classification (98.86% for Laptop dataset).
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%). |
doi_str_mv | 10.1016/j.eswa.2016.06.005 |
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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%).</description><identifier>ISSN: 0957-4174</identifier><identifier>EISSN: 1873-6793</identifier><identifier>DOI: 10.1016/j.eswa.2016.06.005</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Bayesian analysis ; Classification ; Classifiers ; Data mining ; Ensemble learning ; Evolutionary algorithms ; Machine learning ; Mathematical models ; Multiobjective optimization ; Sentiment analysis ; Voting ; Weighted majority voting</subject><ispartof>Expert systems with applications, 2016-11, Vol.62, p.1-16</ispartof><rights>2016 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c399t-946636764cbf13d6b42a02e6495fcbf559997060b412e0b6ea707cb181b598043</citedby><cites>FETCH-LOGICAL-c399t-946636764cbf13d6b42a02e6495fcbf559997060b412e0b6ea707cb181b598043</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.eswa.2016.06.005$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Onan, Aytuğ</creatorcontrib><creatorcontrib>Korukoğlu, Serdar</creatorcontrib><creatorcontrib>Bulut, Hasan</creatorcontrib><title>A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification</title><title>Expert systems with applications</title><description>• A novel multi-objective differential evolution algorithm based classifier ensemble for text sentiment classification.• An empirical comparison of weighted and unweighted voting schemes.• Extensive empirical analysis on metaheuristic based voting schemes for sentiment analysis.• High classification accuracies for text sentiment classification (98.86% for Laptop dataset).
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%).</description><subject>Bayesian analysis</subject><subject>Classification</subject><subject>Classifiers</subject><subject>Data mining</subject><subject>Ensemble learning</subject><subject>Evolutionary algorithms</subject><subject>Machine learning</subject><subject>Mathematical models</subject><subject>Multiobjective optimization</subject><subject>Sentiment analysis</subject><subject>Voting</subject><subject>Weighted majority voting</subject><issn>0957-4174</issn><issn>1873-6793</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9kEFLHDEYhkNpoVvrH_CUo5fZfpnMJBvoRcRaQfCi55BkvlmzZCY2ya725k83w4pH4SMJ4Xlf-B5CzhisGTDxa7fG_GzWbX2voQ70X8iKbSRvhFT8K1mB6mXTMdl9Jz9y3gEwCSBX5PWCTvtQfLQ7dMUfkD6j3z4WHOghFj9vKc4ZJxuQumBy9qPHRK3JFYgzHfw4YsK5eBMoHmLY16qZmrCNyZfHiY4x0YIvheYFmurx0ePMwv4k30YTMp6-3yfk4c_V_eXf5vbu-uby4rZxXKnSqE4ILqTonB0ZH4TtWgMtik71Y_3qe6WUBAG2Yy2CFWgkSGfZhtlebaDjJ-T82PuU4r895qInnx2GYGaM-6zZhveCK8F4Rdsj6lLMOeGon5KfTPqvGehFt97pRbdedGuoA30N_T6GsC5xqJJ0dh5nh4NP1aweov8s_gYooowG</recordid><startdate>20161115</startdate><enddate>20161115</enddate><creator>Onan, Aytuğ</creator><creator>Korukoğlu, Serdar</creator><creator>Bulut, Hasan</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20161115</creationdate><title>A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification</title><author>Onan, Aytuğ ; Korukoğlu, Serdar ; Bulut, Hasan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c399t-946636764cbf13d6b42a02e6495fcbf559997060b412e0b6ea707cb181b598043</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Bayesian analysis</topic><topic>Classification</topic><topic>Classifiers</topic><topic>Data mining</topic><topic>Ensemble learning</topic><topic>Evolutionary algorithms</topic><topic>Machine learning</topic><topic>Mathematical models</topic><topic>Multiobjective optimization</topic><topic>Sentiment analysis</topic><topic>Voting</topic><topic>Weighted majority voting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Onan, Aytuğ</creatorcontrib><creatorcontrib>Korukoğlu, Serdar</creatorcontrib><creatorcontrib>Bulut, Hasan</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Expert systems with applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Onan, Aytuğ</au><au>Korukoğlu, Serdar</au><au>Bulut, Hasan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification</atitle><jtitle>Expert systems with applications</jtitle><date>2016-11-15</date><risdate>2016</risdate><volume>62</volume><spage>1</spage><epage>16</epage><pages>1-16</pages><issn>0957-4174</issn><eissn>1873-6793</eissn><abstract>• A novel multi-objective differential evolution algorithm based classifier ensemble for text sentiment classification.• An empirical comparison of weighted and unweighted voting schemes.• Extensive empirical analysis on metaheuristic based voting schemes for sentiment analysis.• High classification accuracies for text sentiment classification (98.86% for Laptop dataset).
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%).</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.eswa.2016.06.005</doi><tpages>16</tpages></addata></record> |
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subjects | Bayesian analysis Classification Classifiers Data mining Ensemble learning Evolutionary algorithms Machine learning Mathematical models Multiobjective optimization Sentiment analysis Voting Weighted majority voting |
title | A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification |
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