Journal Article10.1007/S10589-005-3907-9
A Dual-Objective Evolutionary Algorithm for Rules Extraction in Data Mining
Kay Chen Tan,Qiang Yu,Ji Hua Ang +2 more
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TL;DR: A dual-objective evolutionary algorithm for extracting multiple decision rule lists in data mining, which aims at satisfying the classification criteria of high accuracy and ease of user comprehension.
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Abstract: This paper presents a dual-objective evolutionary algorithm (DOEA) for extracting multiple decision rule lists in data mining, which aims at satisfying the classification criteria of high accuracy and ease of user comprehension. Unlike existing approaches, the algorithm incorporates the concept of Pareto dominance to evolve a set of non-dominated decision rule lists each having different classification accuracy and number of rules over a specified range. The classification results of DOEA are analyzed and compared with existing rule-based and non-rule based classifiers based upon 8 test problems obtained from UCI Machine Learning Repository. It is shown that the DOEA produces comprehensible rules with competitive classification accuracy as compared to many methods in literature. Results obtained from box plots and t-tests further examine its invariance to random partition of datasets.
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Citations
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
An evolutionary memetic algorithm for rule extraction
TL;DR: An Evolutionary Memetic Algorithm (EMA), which uses a local search intensity scheme to complement the global search capability of Evolutionary Algorithms (EAs), is proposed for rule extraction.
58
A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner
Lale Özbakır,Adil Baykasoğlu,Sinem Kulluk +2 more
- 01 Jan 2010
TL;DR: Experimental study on the benchmark data sets and comparisons with some other classical and state-of-the art rule extraction algorithms has shown that the proposed approach has a big potential to discover more accurate and concise classification rules.
52
MEPAR-miner: Multi-expression programming for classification rule mining
Adil Baykasoğlu,Lale Özbakır +1 more
TL;DR: A new chromosome representation and solution technique based on Multi-Expression Programming (MEP) which is named as MEPAR-miner (Multi-expression Programming for Association Rule Mining) for rule induction is proposed, which can discover effective classification rules that are as good as (or better than) the ones obtained by the traditional rule induction methods.
48
A novel classification model for cotton yarn quality based on trained neural network using genetic algorithm
TL;DR: A novel classification model for cotton yarn quality is introduced which is composed of two major techniques namely: Artificial Neural Network (ANN) and genetic algorithm (GA).
33
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