文章基本信息

标题：Neural Techniques for Improving the Classification Accuracy of Microarray Data Set using Rough Set Feature Selection Method
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作者：Bichitrananda Patra ; Sujata Dash ; B. K. Tripathy 等
期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2013
卷号：4
期号：3-3
出版社：Seventh Sense Research Group
摘要：Classification, a data mining task is an effective method to classify the data in the process of Knowledge Data Discovery. Classification method algorithms are widely used in medical field to classify the medical data for diagnosis. Feature Selection increases the accuracy of the Classifier because it eliminates irrelevant attributes. This paper analyzes the performance of neural network classifiers with and without feature selection in terms of accuracy and efficiency to build a model on four different datasets. This paper provides rough feature selection scheme, and evaluates the relative performance of four different neural network classification procedures such as Learning Vector Quantisation (LVQ) LVQ1, LVQ3.
关键词：Data Mining; Rough; Feature Selection; Learning Vector Quantisation; Self-Organizing Map; Classification