Improving Simplified Fuzzy ARTMAP Performance Using Genetic Algorithm for Brain Fingerprint Classification

Ramaswamy Palaniappan, Shankar M. Krishnan, Chikkanan Eswaran
{"title":"Improving Simplified Fuzzy ARTMAP Performance Using Genetic Algorithm for Brain Fingerprint Classification","authors":"Ramaswamy Palaniappan, Shankar M. Krishnan, Chikkanan Eswaran","doi":"10.1109/ADCOM.2006.4289909","DOIUrl":null,"url":null,"abstract":"A genetic algorithm is proposed for ordering the input patterns during training for simplified fuzzy ARTMAP (SFA) classifier to improve the individual identification classification performance using brain fingerprints. The results indicate improved classification performance as compared to the existing methods for pattern ordering, namely voting strategy and min-max. As the ordering method is general, it could be used with any dataset to obtain improved classification performance when SFA is used.","PeriodicalId":296627,"journal":{"name":"2006 International Conference on Advanced Computing and Communications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 International Conference on Advanced Computing and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ADCOM.2006.4289909","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

A genetic algorithm is proposed for ordering the input patterns during training for simplified fuzzy ARTMAP (SFA) classifier to improve the individual identification classification performance using brain fingerprints. The results indicate improved classification performance as compared to the existing methods for pattern ordering, namely voting strategy and min-max. As the ordering method is general, it could be used with any dataset to obtain improved classification performance when SFA is used.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用遗传算法改进简化模糊ARTMAP脑指纹分类性能
提出了一种遗传算法对简化模糊ARTMAP (SFA)分类器训练过程中的输入模式进行排序,以提高基于脑指纹的个体识别分类性能。结果表明,与现有的模式排序方法(即投票策略和最小-最大)相比,该方法的分类性能有所提高。由于排序方法是通用的,因此在使用SFA时,它可以用于任何数据集,以获得更好的分类性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Message Integrity in the World Wide Web: Use of Nested Hash Function and a Fast Stream Cipher Feature Extraction Learning for Stereovision Based Robot Navigation System Semantics for a Distributed Programming Language Using SACS and Weakest Pre-Conditions On Evaluating Obfuscatory Strength of Alias-based Transforms using Static Analysis A Multi-Algorithmic Face Recognition System
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1