Multiple Classification Using SVM Based Multi Knowledge Based System

Thirumalaimuthu Thirumalaiappan Ramanathan; Dharmendra Sharma

doi:10.1016/j.procs.2017.09.139

Multiple Classification Using SVM Based Multi Knowledge Based System

Thirumalaimuthu Thirumalaiappan Ramanathan
, Dharmendra Sharma

Information Systems

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

69 Downloads (Pure)

Abstract

Support Vector Machine (SVM) is a machine learning classification technique that supports binary classification. In the recent years, efforts are made to extend the SVM algorithm to support multiple classifications. This paper presents a SVM based multi-knowledge-based system (SMK) design that supports multiple classifications. The proposed design is successfully tested on a classification problem. The benchmark car evaluation dataset from UCI machine learning repository is used for training and testing the SMK. The SMK shows good performance on this classification and shows good promise for the future.

Original language	English
Pages (from-to)	307-311
Number of pages	5
Journal	Procedia Computer Science
Volume	115
DOIs	https://doi.org/10.1016/j.procs.2017.09.139
Publication status	Published - 2017
Event	7th International Conference on Advances in Computing and Communications, ICACC 2017 - Kochi, Kerala, India Duration: 22 Aug 2017 → 24 Aug 2017

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title = "Multiple Classification Using SVM Based Multi Knowledge Based System",

abstract = "Support Vector Machine (SVM) is a machine learning classification technique that supports binary classification. In the recent years, efforts are made to extend the SVM algorithm to support multiple classifications. This paper presents a SVM based multi-knowledge-based system (SMK) design that supports multiple classifications. The proposed design is successfully tested on a classification problem. The benchmark car evaluation dataset from UCI machine learning repository is used for training and testing the SMK. The SMK shows good performance on this classification and shows good promise for the future.",

keywords = "knowledge-based system, Support vector machine",

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AB - Support Vector Machine (SVM) is a machine learning classification technique that supports binary classification. In the recent years, efforts are made to extend the SVM algorithm to support multiple classifications. This paper presents a SVM based multi-knowledge-based system (SMK) design that supports multiple classifications. The proposed design is successfully tested on a classification problem. The benchmark car evaluation dataset from UCI machine learning repository is used for training and testing the SMK. The SMK shows good performance on this classification and shows good promise for the future.

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KW - Support vector machine

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JF - Procedia Computer Science

T2 - 7th International Conference on Advances in Computing and Communications, ICACC 2017

Y2 - 22 August 2017 through 24 August 2017

ER -

Multiple Classification Using SVM Based Multi Knowledge Based System

Abstract

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