Contribution to Book
A Comparison of Supervised Learning Techniques for Clustering
Neural Information Processing
(2015)
Abstract
The significance of data mining has experienced dramatic growth over the past few years. This growth has been so drastic that many industries and academic disciplines apply data mining in some form. Data mining is a broad subject that encompasses several topics and problems; however this paper will focus on the supervised learning classification problem and discovering ways to optimize the classification process. Four classification techniques (naive Bayes, support vector machine, decision tree, and random forest) were studied and applied to data sets from the UCI Machine Learning Repository. A Classification Learning Toolbox (CLT) was developed using the R statistical programming language to analyze the date sets and report the relationships and prediction accuracy between the four classifiers.
Keywords
- Classification technique,
- Supervised learning,
- Data mining,
- Naive Bayes,
- Decision tree,
- Random forest,
- Support vector machine
Disciplines
Publication Date
2015
Editor
Sabri Arik, Tingwen Huang, Weng Kin Lai, Qingshan Liu
Series
Lecture Notes in Computer Science
DOI
10.1007/978-3-319-26532-2_52
Citation Information
William Ezekiel and Umashanger Thayasivam. "A Comparison of Supervised Learning Techniques for Clustering" Neural Information Processing Vol. 9489 (2015) p. 476 - 483 Available at: http://works.bepress.com/umashanger-thayasivam/4/