Data Streams classification using Local Concept-adapted IOLIN System (지역적 컨셉트 적응형 IOLIN시스템을 사용한 데이터 스트림의 분류)

김재우; Jaewon Song (송재원); Ju-Hong Lee (이주홍)

Data Streams classification using Local Concept-adapted IOLIN System

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2008, 13(1), pp.37-44
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

김재우 ¹, Jaewon Song ¹, Ju-Hong Lee ¹

¹인하대학교

Accredited

ABSTRACT

Data stream has the tendency to change in patterns over time. Also known as concept drift, such problem can reduce the predictive performance of a classification model. CVFDT and IOLIN tried to solve the problem of a concept drift through incremental classification model updates. The local changes in patterns, however, was revealed to be unable to resolve the problems of local concept drift that occurs by influencing on total classification results. In this paper, we propose adapted IOLIN system that improves system's predictive performance by detecting the local concept drift. The experimental result shows that adaptive IOLIN, the proposed method, is about 2.8% in accuracy better than IOLIN and about 11.2% in accuracy better than CVFDT.

KEYWORDS

실시간 데이터 마이닝(Real-Time Data Mining), 온라인 학습(Online Learning), 컨셉트 변화(Concept Drift), 정보 퍼지 네트워크(Info-Fuzzy Network).

Citation status

* References for papers published after 2023 are currently being built.

[book] C. Aggarwal / 2007 / Data Streams: Models and Algorithms, 354Page / Springer

[book] A. Tsymbal / 2004 / The problem of concept drift: definitions and related work, Technical Report TCD-CS-2004-15, Department of Computer Science / Trinity College Dublin

[journal] G. Widmer / 1996 / Learning in the Presence of Concept Drift and Hidden Contexts / Machine Learning 23(1) : 69~101

[confproc] C. Aggarwal / 2003 / A Framework for Diagnosing Changes in Evolving Data Streams / Proceedings of the ACM SIGKDD Conference

[confproc] G. Hulten / 2001 / Mining Time-Changing Data Streams / Proc. of KDD 2001 : 97~106

[journal] M. Last / 2002 / Online Classification of Nonstationary Data Streams / Intelligent Data Analysis 6(2) : 129~147

[confproc] L. Cohen / 2004 / Incremental Info-Fuzzy Algorithm for Real Time Data Mining of Non-Stationary Data Streams / TDM Workshop, Brighton

[confproc] P. Domingos / 2000 / Mining high-speed data streams / In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : 71~80

[book] O. Maimon / 2000 / Knowledge Discovery and Data Mining - The Info-Fuzzy Network (IFN) Methodology / Kluwer Academic Publishers

[confproc] L. Cohen / 2005 / Incremental Classification of Nonstationary Data Streams / Proceedings of the Second International Workshop on Knowledge Discovery in Data Streams : 117~124

[journal] R. Klinkenberg / 2004 / Learning drifting concepts: example selection vs. example weighting, Intelligent Data Analysis / Systems Capable of Dealing with Concept Drift 8(3)

[confproc] H. Wang / 2003 / Mining concept-drifting data streams using ensemble classifiers / Proc. 9th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining KDD-2003 : 226~235

[web] / daisin securities web site at / http://www.daishin. co.kr/

KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65