Enhancing Classification Accuracy by Attribute Reduction Technique
A. Rama1, C. Nalini2

1A.Rama, Assistant Professor, Department of Information Technology, Bharath Institute of Higher Education and Research
2Dr. C. Nalini, Professor, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research

Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 1248-1250 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7951068519/19©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: Data mining methods helps in analyzing the data set efficiently by reducing the size of the search space, so as to choose significant attribute for recognition of the type of appropriate data. This study deals with the classification of categories of glass, which helps in criminology investigation. The glass material got as evidence in the crime scene is to be correctly identified. The fundamental purpose of this work is to deal with a large glass data set with high accuracy of identifying king of the glass. The models are constructed with supervised learning algorithm in weka tool. It is important to minimize the dimensions of data by constructing the models by selecting the attributes that is implemented as search methods, which are applied to predict the evaluating for the possible test cases.
Keywords: Data mining, Bayes algorithm, Glass data set, Search Methods, Tree, Classification, Meta boost.

Scope of the Article: Data mining