###
:2019,28(11):138-146
本文二维码信息
码上扫一扫!
基于微局部特征的时序数据二分类算法
(中国科学技术大学 计算机科学与技术学院, 合肥 230027)
Time Series Binary Classification Based on Mini Local Features
(School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China)
摘要
图/表
参考文献
相似文献
本文已被:浏览 23次   下载 13
投稿时间:2019-03-25    修订日期:2019-04-18
中文摘要: 在诸多时序数据分类算法中,有一类算法借助时序数据的局部特征对时序数据进行分类,它们取得了不错的分类结果,然而其时间复杂度以及分类精度依旧存在可见的提升空间.本文提出的微局部特征二分类算法,着眼于局部特征本身的性质,对局部特征集进行限制,进而改进现有的基于局部特征的分类算法.新算法通过理论分析支撑,将经典算法的局部特征集大幅缩小,进而显著提升了分类算法的时间性能.另一方面通过重定义局部特征的评价标准,新算法选出性质更为优良的局部特征,提升了分类精度.
Abstract:Among all kinds of time series classification algorithm, algorithms based on local features of time series data have achieved reasonable results. However, there is still abundant space for improvements of them in time complexity and accuracy. In this study, we propose an improved algorithm based on local features. It focuses on the property of local features and put restrictions on the set of local features. On the one hand, supported by theoretical analysis, our new algorithm cuts the size of set of local features and consequently reduces the time and space complexity. On the other hand, we redefine the criteria of selecting local features so that we can select more discriminative local features.
文章编号:     中图分类号:    文献标志码:
基金项目:
引用文本:
舒伟博.基于微局部特征的时序数据二分类算法.计算机系统应用,2019,28(11):138-146
SHU Wei-Bo.Time Series Binary Classification Based on Mini Local Features.COMPUTER SYSTEMS APPLICATIONS,2019,28(11):138-146

用微信扫一扫

用微信扫一扫