Neural Techniques for Improving the Classification Accuracy of Microarray Data Set using Rough Set Feature Selection Method

-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, optimizedlearning-rate LVQ1 (OLVQ1), and The Self-Organizing Map (SOM) incorporating those methods. Experimental results show that the LVQ3 neural classification is an appropriate classification method makes it possible to construct high performance classification models for microarray data. Keywords-Data Mining, Rough, Feature Selection, Learning Vector Quantisation, Self-Organizing Map, Classification.