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

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Summary

Why using LCS?

[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]

Proposed method
The preprocessing steps
Result compared
Rule compaction strategies
Comparisons and experimental results
Conclusion
Findings
Compliance with ethical standards
Full Text
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