NDLI: Classification rule discovery using learning automata

Content Provider	Springer Nature Link
Author	Zahiri, Seyed Hamid
Copyright Year	2011
Abstract	Learning automata (LA) are adaptive decision making units that can learn to choose the optimal action from a set of actions by interaction with an environment (search space). This article proposes the use of the learning automata as a new tool for data mining (namely LA-miner). The basic scheme is utilizing LA as an effective optimizer for searching the rule-set space. In fact, LA-miner searches the rule-set space to discover an effective rule-set which maximizes a predefined fitness function. The fitness function is related to the total true positives, false positives, true negatives, and false negatives. Extensive experimental results on different kinds of benchmarks with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the powerfulness of the proposed method. The comparative results illustrate that the performance of the proposed LA-miner is comparable to, sometimes better than those of the CN2 (a traditional data mining method) and similar approaches which are designed based on the swarm intelligence algorithms (ant colony optimization and particle swarm optimization) and an evolutionary algorithm (genetic algorithm).
Starting Page	205
Ending Page	213
Page Count	9
File Format	PDF
ISSN	18688071
Journal	International Journal of Machine Learning and Cybernetics
Volume Number	3
Issue Number	3
e-ISSN	1868808X
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-11-17
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Learning automata Rule based classifiers Data mining Pattern Recognition Systems Biology Control, Robotics, Mechatronics Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics) Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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