Paper Title:
A Comparison of Machine Learning Classifiers
  Abstract

A number of different classifiers have been used to improve the precision and accuracy and give better classification results. Machine learning classifiers have proven to be the most successful techniques in majority of the fields. This paper presents a comparison of the three most successful machine learning classification techniques SVM, boosting and Local SVM applied to a cancer dataset. The comparison is made on the basis of precision and accuracy along with the training time analysis. Finally, the efficacy of the classifiers is found.

  Info
Periodical
Advanced Materials Research (Volumes 271-273)
Edited by
Junqiao Xiong
Pages
149-153
DOI
10.4028/www.scientific.net/AMR.271-273.149
Citation
P. Srikanth, A. Singh, D. Kumar, A. Nagrare, V. Angoth, "A Comparison of Machine Learning Classifiers", Advanced Materials Research, Vols. 271-273, pp. 149-153, 2011
Online since
July 2011
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Jia Jun Li, Li Ping Qin, Jia Zhao
Abstract:To achieve low costs and better accuracy of individual risk assessments, we constructed a practical method based on multiple classifiers. The...
116
Authors: Kai Li, Hong Tao Gao
Abstract:To improve the generalization performance for ensemble learning, a subgraph based selective classifier ensemble algorithm is presented....
261
Authors: Dech Thammasiri, Phayung Meesad
Chapter 13: Modeling, Analysis and Simulation of Manufacturing Processes
Abstract:In this research we propose an ensemble classification technique based on decision tree, artificial neural network, and support vector...
3682
Authors: Dech Thammasiri, Phayung Meesad
Chapter 17: Metrology and Measurement
Abstract:In this research we propose an ensemble classification technique base on creating classification from a variety of techniques such as...
6572
Authors: Bao Yu Dong, Guang Ren
Chapter 5: Control, Automation and Computer-Aided Systems
Abstract:This paper presents a novel method of analog circuit fault diagnosis using AdaBoost with SVM-based component classifiers. We use binary-SVMs...
1414