Feature Selection Based on Information Gain
B. Azhagusundari1, Antony Selvadoss Thanamani2
1B. Azhagusundari, Ph.D Research Scholar, Nallamuthu Gounder Mahalingam College, Pollachi, Coimbatore, India.
2Dr.Antony Selvadoss Thanamani, Professor and Head, Department of Computer Science, Nallamuthu Gounder Mahalingam College, Pollachi, Coimbatore, India.
Manuscript received on 09 January 2013 | Revised Manuscript received on 18 January 2013 | Manuscript Published on 30 January 2013 | PP: 18-21 | Volume-2 Issue-2, January 2013 | Retrieval Number: B0352012213 /2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The attribute reduction is one of the key processes for knowledge acquisition. Some data set is multidimensional and larger in size. If that data set is used for classification it may end with wrong results and it may also occupy more resources especially in terms of time. Most of the features present are redundant and inconsistent and affect the classification. In order to improve the efficiency of classification these redundancy and inconsistency features must be eliminated. This paper discusses an algorithm based on discernibility matrix and Information gain to reduce attributes.
Keywords: Attribute Reduction, Discernibility matrix, Information Gain
Scope of the Article: Classification