Several classifiers for intruder detection applications

Elena Roxana Buhus, L. Grama, C. Rusu
{"title":"Several classifiers for intruder detection applications","authors":"Elena Roxana Buhus, L. Grama, C. Rusu","doi":"10.1109/SPED.2017.7990432","DOIUrl":null,"url":null,"abstract":"The goal of this work is to present some possible intruder detection systems and the influence of impulse-like signals upon the overall classification accuracy. Two different scenarios are used: in the first scenario five sound classes are considered (last class belong to impulsive sounds — gunshots), while in the second scenario we dropped out the impulsive sound class. More classifiers are used in both scenarios and different number of features are considered. An improvement in the classification accuracy is obtained within the second scenario. The highest accuracy for the first scenario is for J48 classifier using 51 features, while for the second scenario the highest accuracy is attained for Simple Logistic classifier wit 101 features.","PeriodicalId":345314,"journal":{"name":"2017 International Conference on Speech Technology and Human-Computer Dialogue (SpeD)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Speech Technology and Human-Computer Dialogue (SpeD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPED.2017.7990432","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

The goal of this work is to present some possible intruder detection systems and the influence of impulse-like signals upon the overall classification accuracy. Two different scenarios are used: in the first scenario five sound classes are considered (last class belong to impulsive sounds — gunshots), while in the second scenario we dropped out the impulsive sound class. More classifiers are used in both scenarios and different number of features are considered. An improvement in the classification accuracy is obtained within the second scenario. The highest accuracy for the first scenario is for J48 classifier using 51 features, while for the second scenario the highest accuracy is attained for Simple Logistic classifier wit 101 features.
用于入侵者检测应用程序的几个分类器
本工作的目的是提出一些可能的入侵者检测系统以及类脉冲信号对整体分类精度的影响。我们使用了两个不同的场景:在第一个场景中,我们考虑了五个声音类别(最后一个类别属于冲动性声音——枪声),而在第二个场景中,我们放弃了冲动性声音类别。在这两种情况下都使用了更多的分类器,并且考虑了不同数量的特征。在第二个场景中,分类精度得到了提高。第一个场景的最高准确率是使用51个特征的J48分类器,而第二个场景的最高准确率是使用101个特征的Simple Logistic分类器。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信