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This new algorithm is based on three major processes: (i) making an assumption regarding whether features are dependent on or independent of each other, (ii) computing the amount of information of features when it is assumed that they are dependent on each other and then sorting them in a descending manner based on the amount of information, (iii) speeding up the algorithm by optimizing the forward search algorithm that is used in the construction of the final hypothesis from base learner hypotheses. As a result of these processes, it has been seen in the experiments that choosing the relevant assumption can boost learning performance if features are independent of each other; considering features according to the amount of information provides high accuracy and diversity of base learner models. According to experiment results, the algorithm that has been developed has the highest average classification accuracy rate across the 33 datasets. The highest and the lowest average classification accuracy rates have been found to be 89.80 and 78.03%, respectively.<\/jats:p>","DOI":"10.1093\/comjnl\/bxz118","type":"journal-article","created":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T19:26:39Z","timestamp":1567625199000},"page":"1756-1774","source":"Crossref","is-referenced-by-count":11,"title":["The Construction of a Majority-Voting Ensemble Based on the Interrelation and Amount of Information of Features"],"prefix":"10.1093","volume":"63","author":[{"given":"Fatih","family":"Ayd\u0131n","sequence":"first","affiliation":[{"name":"Department of Software Engineering, Faculty of Engineering, Kirklareli University, Kirklareli, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zafer","family":"Aslan","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Istanbul Aydin University, Istanbul, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,11,15]]},"reference":[{"key":"2020111609452982700_ref1","volume-title":"Machine Learning, Neural and Statistical Classification","author":"Michie","year":"2009","edition":"2009th"},{"key":"2020111609452982700_ref2","volume-title":"Introduction to Machine Learning","author":"Alpaydin","year":"2014","edition":"3rd"},{"key":"2020111609452982700_ref3","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1162\/neco.1996.8.7.1341","article-title":"The lack of a priori distinctions between learning algorithms","volume":"8","author":"Wolpert","year":"1996","journal-title":"Neural Comput."},{"key":"2020111609452982700_ref4","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","article-title":"No free lunch theorems for optimization","volume":"1","author":"Wolpert","year":"1997","journal-title":"IEEE Trans. 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