Indian Journal of Science and Technology
Year: 2017, Volume: 10, Issue: 4, Pages: 1-6
Rajesh Vemulakonda1 , Abdul Ahad2 , Suresh Babu Yalavarthi3 , Praneeth Cheraku4 and Nageswara Rao Puli4
1Department of CSE, P V P Siddhartha Institute of Technology, Vijayawada − 520007, Andhra Pradesh, India; [email protected] 2Department of CSE, ANU College, Guntur − 522510, Andhra Pradesh, India; [email protected] 3Department of CSE, JKC College, Guntur − 522006, Andhra Pradesh, India; [email protected] 4Department of CSE, SRK Institute of Technology, Vijayawada − 521108, Andhra Pradesh, India; [email protected], [email protected]
Objectives: Knowledge Discovery methods get more accurate results when the dimensionality of the data is subsided; dimensionality is an important aspect of any data. Several algorithms have been proposed to increase the accuracy, but most of them generate complex models as the size of the data is extremely large. Objective of this paper is to build a simple model to get high accuracy. Method: In order to increase the accuracy of the Knowledge Discovery methods by substituting the dimensionality, we introduce a novel heuristic functionality, Arbitrary Gini Index (ArGI). Findings: We evaluated the performance of ArGI on the real world datasets. The experiment on the ten real world data sets analysis shows 60% data sets are more accurate for ArGI and 40% for Gini Index. Applications: It is expecting that the applications of ArGI will show a better approach in the real world learning tasks.
Keywords: Arbitrary Gini Index, CART, Classification, Datasets, Decision Tree, Filtering, Random Sampling
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