New Approaches for Multiclass Classification
Shankhpal S.V.1, Dhawas N.A2
1Shankhpal S.V., Received B.E. Degree in Computer Engineering and pursuing M.E. Computer Engineering at SIT, Lonavala, Maharashtra, India.
2Prof. Dhawas N.A., HOD of IT Department at SIT, Lonavala, Maharashtra, India.
Manuscript received on June 25, 2014. | Revised Manuscript received on July 03, 2014. | Manuscript published on July 05, 2014. | PP: 101-104 | Volume-4, Issue-3, July 2014. | Retrieval Number: C2318074314/2012©BEIESP
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© The Authors. Published By: 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: Classification is very important in data mining. It is nothing but categorization of data for its most effective and efficient use. In basic approach to storing data, data can be classified according its importance or how often it needs to be accessed decision tree is one of the classification technique. Decision tree is used to clarify and find solution to complex problem. Structure of decision tree contains multiple possible solutions and displays it in a simple, easy to understand format. There is different algorithm used for classification. In this paper tree is constructed using the geometric structure of data. It builds small decision trees and gives better performance. Now we will use adaptive boosting method for boosting decision tree so it improving the accuracy of decision tree.
Keywords: GDT, Multiclass classification, Oblique decision tree, SVM