Impact of Instance Reduction Filters on Ensembled Decision Tree Classifier
G. Sujatha1, K.Usha Rani2
1G.Sujatha, Research scholar, Department of Computer Science, Sri Padmavati Mahila Visvavidyalayam, Tirupati (AP), India.
2K. Usha Rani, Department of Computer Science, Sri Padmavati Mahila Visvavidyalayam, Tirupati (AP), India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 2719-2723 | Volume-8 Issue-8, June 2019 | Retrieval Number: H7285068819/19©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 is the process of discovering data from different perspectives and converts into useful information. Among various Data Mining techniques Classification is the most prominent supervised learning technique in Data Mining. Decision Trees are one among supervised learning techniques, plays vital role and widely used in medical domain to diagnose the problem of the patient. The performance of the Decision Tree classification increases by efficient Data Reduction Techniques. Instance Reduction is one among various techniques for Data Reduction. Supervised and Unsupervised Instance Filters are key approaches for Instance Reduction Filters. In this paper the experiment is conducted through Hybrid Instance Reduction Filters on Tumor Datasets.
Keyword: Classification, Decision Tree, Instance Reduction Filters, Multiboot, Tumor Datasets.
Scope of the Article: Social Sciences.