Learning Decision Trees from Data Streams with Concept Drift | Zendy

Dariusz Jankowski | Zendy; Konrad Jackowski | Zendy; Bogusław Cyganek | Zendy

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Learning Decision Trees from Data Streams with Concept Drift

Author(s) -

Dariusz Jankowski,

Konrad Jackowski,

Bogusław Cyganek

Publication year - 2016

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2016.05.508

Subject(s) - computer science , concept drift , incremental decision tree , novelty , decision tree , data stream mining , tree (set theory) , machine learning , data stream , data mining , decision tree learning , artificial intelligence , evolutionary algorithm , task (project management) , population , term (time) , id3 algorithm , theology , mathematical analysis , telecommunications , philosophy , physics , demography , mathematics , management , quantum mechanics , sociology , economics

This paper addresses a data mining task of classifying data stream with concept drift. The proposed algorithm, named Concept-adapting Evolutionary Algorithm For Decision Tree does not require any knowledge of the environment such as numbers and rates of drifts. The novelty of the approach is combining tree learner and evolutionary algorithm, where the decision tree is learned incrementally and all information is stored in an internal structure of the trees’ population. The proposed algorithm is experimentally compared with state-of-the-art stream methods on several real live and synthetic datasets. Results indicate its high performance in term of accuracy and processing time

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