Superiority demonstration of variance-considered machines by comparing error rate with support vector machines |
| |
Authors: | Yeom Hong-Gi Park Seung-Min Park Junheong Sim Kwee-Bo |
| |
Affiliation: | (1) Data Mining and Modeling Capability Corporate R&D, The Dow Chemical Company, 2301 N. Brazosport Blvd., B-1226, Freeport, TX 77541, USA;; |
| |
Abstract: | To improve the performance of classification algorithms, we proposed a new varianceconsidered machine (VCM) classification
algorithm in a previous study. The study showed theoretically that VCMs have lower error probabilities than SVMs. The purpose
of this paper is to experimentally demonstrate the superiority of VCMs. Therefore, we verified our proposal with several case
experiments using data following a Gaussian distribution with different variances and prior probabilities. To estimate performance,
the experiment for each case was executed 1000 times and the error rates were averaged for accuracy. The data of each experiment
have different distances between means of data, and different ratios between training data and testing data. Thus, we proved
that the error rate of VCMs is lower than the error rate of SVMs, although their performances were not similar in each case.
Consequently, we expect that VCMs will be applied to a variety fields. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|