Constrained classifier: a novel approach to nonlinear classification

Simple classifiers have the advantage of more generalization capability with the side effect of less power. It would be a good idea if we could build a classifier which is as simple as possible while giving it the ability of classifying complex patterns. In this paper, a hybrid classifier called “co...

Full description

Saved in:
Bibliographic Details
Published in:Neural computing & applications 2013-12, Vol.23 (7-8), p.2367-2377
Main Authors: Abbassi, H., Monsefi, R., Sadoghi Yazdi, H.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science
control theory
systems
Data Mining and Knowledge Discovery
Data processing. List processing. Character string processing
Exact sciences and technology
Image Processing and Computer Vision
Memory organisation. Data processing
Original Article
Probability and Statistics in Computer Science
Software
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Simple classifiers have the advantage of more generalization capability with the side effect of less power. It would be a good idea if we could build a classifier which is as simple as possible while giving it the ability of classifying complex patterns. In this paper, a hybrid classifier called “constrained classifier” is presented that classifies most of the input patterns using a simple, for example, a linear classifier. It performs the classification in four steps. In the “Dividing” step, the input patterns are divided into linearly separable and nonlinearly separable groups. The patterns belonging to the first group are classified using a simple classifier while the second group patterns (named “constraints”) are modeled in the “Modeling” step. The results of previous steps are merged together in the “Combining” step. The “Evaluation” step tests and fine tunes the membership of patterns into two groups. The experimental results of comparison of the new classifier with famous classifiers such as “support vector machine”, k-NN, and “Classification and Regression Trees” are very encouraging.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-012-1194-9