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Multiple labels associative classification

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Abstract

Building fast and accurate classifiers for large-scale databases is an important task in data mining. There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. In this paper, the problem of producing rules with multiple labels is investigated, and we propose a multi-class, multi-label associative classification approach (MMAC). In addition, four measures are presented in this paper for evaluating the accuracy of classification approaches to a wide range of traditional and multi-label classification problems. Results for 19 different data sets from the UCI data collection and nine hyperheuristic scheduling runs show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches.

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Authors and Affiliations

  1. Department of Computing, MOSAIC Research Centre, School of Informatics, University of Bradford, Bradford, BD7 1DP, UK

    Fadi Abdeljaber Thabtah & Peter Cowling

  2. Department of Computing, School of Informatics, University of Bradford, Bradford, BD7 1DP, UK

    Yonghong Peng

Authors
  1. Fadi Abdeljaber Thabtah
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  2. Peter Cowling
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  3. Yonghong Peng
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Correspondence to Fadi Abdeljaber Thabtah.

Additional information

Fadi Abdeljaber Thabtah received a B.S. degree in Computer Science from Philadelphia University, Jordan, in 1997 and an M.S. degree in Computer Science from California State University, USA in 2001. From 1996 to 2001, he worked as professional in database programming and administration in United Insurance Ltd. in Amman. In 2002, he started his academic career and joined the Philadelphia University as a lecturer. He is currently a final graduate student at the Department of Computer Science, Bradford University, UK. He has published about seven scientific papers in the areas of data mining and machine learning. His research interests include machine learning, data mining, artificial intelligence and object-oriented databases.

Peter Cowling is a Professor of Computing at the University of Bradford. He obtained M.A. and D.Phil. degrees from the University of Oxford. He leads the Modelling Optimisation Scheduling And Intelligent Control (MOSAIC) research centre (http://mosaic.ac), whose main research interests lie in the investigation and development of new modelling, optimisation, control and decision support technologies, which bridge the gap between theory and practice. Applications include production and personnel scheduling, intelligent game agents and data mining. He has published over 40 scientific papers in these areas and is active as a consultant to industry.

Yonghong Peng's research areas include machine learning and data mining, and bioinformatics. He has published more than 35 scientific papers in related areas. Dr. Peng is a member of the IEEE and Computer Society, and has been a member of the programme committee of several conferences and workshops. Dr. Peng referees papers for several journals including the IEEE Trans. on Systems, Man and Cybernetics (part C), IEEE Trans. on Evolutionary Computation, Journal of Fuzzy Sets and Systems, Journal of Bioinformatics, and Journal of Data Mining and Knowledge Discovery, and is refereeing papers for several conferences.

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Thabtah, F.A., Cowling, P. & Peng, Y. Multiple labels associative classification. Knowl Inf Syst 9, 109–129 (2006). https://doi.org/10.1007/s10115-005-0213-x

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