文章基本信息

标题：Improvement on Classification Models of Multiple Classes through Effectual Processes
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作者：Tarik A. Rashid
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2015
卷号：6
期号：7
DOI：10.14569/IJACSA.2015.060709
出版社：Science and Information Society (SAI)
摘要：Classify cases in one of two classes referred to as a binary classification. However, some classification algorithms will allow, of course the use of more than two classes. This research work focuses on improving the results of classification models of multiple classes via some effective techniques. A case study of students’ achievement at Salahadin University is used in this research work. The collected data are pre-processed, cleaned, filtered, normalised, the final data was balanced and randomised, then a combining technique of Naïve Base Classifier and Best First Search algorithms are used to ultimately reduce the number of features in data sets. Finally, a multi-classification task is conducted through some effective classifiers such as K-Nearest Neighbor, Radial Basis Function, and Artificial Neural Network to forecast the students’ performance.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; Non-Balanced Data; Feature Selection; Multiple Classification; Machine Learning Techniques; Student Performance Forecasting