International Journal of Computer
Trends and Technology

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Volume 4 | Issue 3 | Year 2013 | Article Id. IJCTT-V4I3P144 | DOI : https://doi.org/10.14445/22312803/IJCTT-V4I3P144

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

Bichitrananda Patra, Sujata Dash, B. K. Tripathy

Citation :

Bichitrananda Patra, Sujata Dash, B. K. Tripathy, "Neural Techniques for Improving the Classification Accuracy of Microarray Data Set using Rough Set Feature Selection Method," International Journal of Computer Trends and Technology (IJCTT), vol. 4, no. 3, pp. 424-429, 2013. Crossref, https://doi.org/10.14445/22312803/IJCTT-V4I3P144

Abstract

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. 

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