Improving a multi-objective evolutionary algorithm to discover quantitative association rules

被引：13
作者：
Martinez-Ballesteros, M. [1 ]
Troncoso, A. [2 ]
Martinez-Alvarez, F. [2 ]
Riquelme, J. C. [1 ]
机构：
[1] Univ Seville, Dept Comp Sci, ES-41012 Seville, Spain
[2] Univ Pablo Olavide, Div Comp Sci, ES-41013 Seville, Spain
来源：
KNOWLEDGE AND INFORMATION SYSTEMS | 2016年 / 49卷 / 02期
关键词：
Association rules; Data mining; Evolutionary computation; Pareto-optimization; GENETIC ALGORITHM;
D O I：
10.1007/s10115-015-0911-y
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This work aims at correcting flaws existing in multi-objective evolutionary schemes to discover quantitative association rules, specifically those based on the well-known non-dominated sorting genetic algorithm-II (NSGA-II). In particular, a methodology is proposed to find the most suitable configurations based on the set of objectives to optimize and distance measures to rank the non-dominated solutions. First, several quality measures are analyzed to select the best set of them to be optimized. Furthermore, different strategies are applied to replace the crowding distance used by NSGA-II to sort the solutions for each Pareto-front since such distance is not suitable for handling many-objective problems. The proposed enhancements have been integrated into the multi-objective algorithm called MOQAR. Several experiments have been carried out to assess the algorithm's performance by using different configuration settings, and the best ones have been compared to other existing algorithms. The results obtained show a remarkable performance of MOQAR in terms of quality measures.
引用
收藏
页码：481 / 509
页数：29
相关论文
共 37 条
  • [11] Dehuri S., 2006, American Journal of Applied Sciences, V3, P2086, DOI 10.3844/ajassp.2006.2086.2095
  • [12] On the discovery of association rules by means of evolutionary algorithms
    del Jesus, Maria J.
    Gamez, Jose A.
    Gonzalez, Pedro
    Puerta, Jose M.
    [J]. WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY, 2011, 1 (05) : 397 - 415
  • [13] A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability
    Garcia, S.
    Fernandez, A.
    Luengo, J.
    Herrera, F.
    [J]. SOFT COMPUTING, 2009, 13 (10) : 959 - 977
  • [14] Interestingness measures for data mining: A survey
    Geng, Liqiang
    Hamilton, Howard J.
    [J]. ACM COMPUTING SURVEYS, 2006, 38 (03) : 3
  • [15] Multi-objective rule mining using genetic algorithms
    Ghosh, A
    Nath, B
    [J]. INFORMATION SCIENCES, 2004, 163 (1-3) : 123 - 133
  • [16] Guvenir H. Altay, 2000, Bilkent university function approximation repository
  • [17] Köppen M, 2007, LECT NOTES COMPUT SC, V4403, P727
  • [18] Li D, 2004, LECT NOTES ARTIF INT, V3066, P573
  • [19] Grammar-based multi-objective algorithms for mining association rules
    Luna, J. M.
    Romero, J. R.
    Ventura, S.
    [J]. DATA & KNOWLEDGE ENGINEERING, 2013, 86 : 19 - 37
  • [20] Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules
    Luna, Jose M.
    Romero, Jose Raul
    Ventura, Sebastian
    [J]. KNOWLEDGE AND INFORMATION SYSTEMS, 2012, 32 (01) : 53 - 76
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