題名: A granular computing approach to improve large attributes learning 作者: Chang, Fengming M.;Chan, Chien-Chung
貢獻者: Department of Information Science and Applications
關鍵詞: Artificial intelligence;Bayesian networks;Decision trees;Granular
computing;Inference engines;Support vector machines;Artificial Neural Network;Data attributes;Data sets;Learning accuracy;Learning
methods;Machine-learning;Mega-fuzzification;Neuro-Fuzzy 日期: 2009
上傳時間: 2010-04-08T12:35:57Z 出版者: Asia University
摘要: Based on the concept of granular computing, this article proposes a novel Boolean Conversion (BC) method to reduce data attribute number for the purpose of improving the efficiency of learning in artificial
intelligence. Data with large amount of attributes usually cause a system freezes or shuts down. The proposed method combines large amount attributes to smaller number ones by the way of Boolean method. Three data sets are used to compare the learning accuracies and efficiencies by Bayesian networks (BN), C4.5 decision tree, support vector machine (SVM), artificial neural network (ANN), fuzzy neural network (FNN, neuro-fuzzy), and Mega-fuzzification learning methods.
Results indicate that the proposed BC method can improve the efficiency of machine learning and the accuracy is not worse. ©2009 IEEE.
