Abstract
Many different classification algorithms can be use in order to analyze, classify and predict data. Learning classifier system (LCS) which is known as a genetic base machine learning system, combines the machine learning with evolutionary computing and other heuristics to produce an adaptive system that learns to solve a particular problem. This paper uses the Michigan style LCS, in the context of bank customer satisfaction to classify customers into two different groups: unsatisfied/satisfied customers. Three different Rule Compaction strategies are used to compare the rule population’s accuracy and micro/macro population size. The result specifies features that mostly influence prediction.
Highlights
[Learning] Classifier Systems (LCSs) [1, 2] are a kind of Rule-Based system (RBS) [3, 4] with general mechanism for parallel rule processing, adaptive generation of new rules, and testing the effectiveness of existing rules
This paper indicates the reason of using Learning classifier system (LCS) as a Genetic Base Machine Learning (GBML) [7, 8] system for prediction
In Parameter Driven Rule Compaction (PDRC) algorithm these parameters are considered in rule compaction strategy as follows: Find the best rules which have the highest value of the product of accuracy and numerosity and generality
Summary
[Learning] Classifier Systems (LCSs) [1, 2] are a kind of Rule-Based system (RBS) [3, 4] with general mechanism for parallel rule processing, adaptive generation of new rules, and testing the effectiveness of existing rules. This paper indicates the reason of using LCS as a Genetic Base Machine Learning (GBML) [7, 8] system for prediction. 3 and the concept of Rule Compaction and their algorithm is presented, experimental results and evaluation are discussed, and Sect. LCS algorithms in general, constitute a unique alternative to other well-known machine learning strategies that follow the classic paradigm of seeking to identify a ‘best’ model that can individually be applied to the entire dataset. Interpretable: LCS rules are logical IF: statements, interpretable to human [14]
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