Multi-dimensional classification via kNN feature augmentation

	Multi-dimensional classification via kNN feature augmentation
	Jia, Bin-Bin 1,2,3; Zhang, Min-Ling 1,2,4
	2019
会议日期	January 27, 2019 - February 1, 2019
会议地点	Honolulu, HI, United states
关键词	Artificial intelligence Nearest neighbor search Augmentation techniques Classification performance Counting statistics Feature vectors Model dependencies Multi-dimensional classifications Multiple class State of the art
页码	3975-3982
英文摘要	Multi-dimensional classification (MDC) deals with the problem where one instance is associated with multiple class variables, each of which specifies its class membership w.r.t. one specific class space. Existing approaches learn from MDC examples by focusing on modeling dependencies among class variables, while the potential usefulness of manipulating feature space hasn't been investigated. In this paper, a first attempt towards feature manipulation for MDC is proposed which enriches the original feature space with kNNaugmented features. Specifically, simple counting statistics on the class membership of neighboring MDC examples are used to generate augmented feature vector. In this way, discriminative information from class space is encoded into the feature space to help train the multi-dimensional classification model. To validate the effectiveness of the proposed feature augmentation techniques, extensive experiments over eleven benchmark data sets as well as four state-of-the-art MDC approaches are conducted. Experimental results clearly show that, compared to the original feature space, classification performance of existing MDC approaches can be significantly improved by incorporating kNN-augmented features. © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org).
会议录	33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
会议录出版者	AAAI Press
会议录出版地	2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA
语种	英语
资助项目	Fundamental Research Funds for the Central Universities[2242018K40082]
WOS研究方向	Computer Science ; Engineering
WOS记录号	WOS:000485292603122
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/118138]
专题	电气工程与信息工程学院
通讯作者	Zhang, Min-Ling
作者单位	1.Southeast Univ, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing, Jiangsu, Peoples R China 2.Southeast Univ, Sch Comp Sci & Engn, Nanjing 210096, Jiangsu, Peoples R China 3.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Gansu, Peoples R China 4.Collaborat Innovat Ctr Wireless Commun Technol, Nanjing, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Jia, Bin-Bin,Zhang, Min-Ling. Multi-dimensional classification via kNN feature augmentation[C]. 见:. Honolulu, HI, United states. January 27, 2019 - February 1, 2019.