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Improved Ant Colony Classification Algorithm Applied to Membership Classification

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Advances in Swarm Intelligence (ICSI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7928))

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Abstract

Membership classification belongs to target customer analysis in the customer relationship management. For membership classification, an improved ant colony classification algorithm named mAnt-Miner+ is proposed. This algorithm on the basis of Ant-Miner, draws on the idea of mAnt-Miner (Ant-Miner that uses a population of many ants), and introduces a new heuristic strategy. Experimental results show that, in terms of prediction accuracy, mAnt-Miner+ is competitive with Ant-Miner and higher than mAnt-Miner; in terms of running efficiency, mAnt-Miner+ is more efficient than mAnt-Miner and Ant-Miner.

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Author information

Authors and Affiliations

  1. The School of Computer and Information, Hefei University of Technology, Hefei, China

    Hongxing Wu & Kai Sun

Authors
  1. Hongxing Wu
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  2. Kai Sun
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), School of lectronics Engineering and Computer Science, Department of Machine Intelligence, Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, 215123, Suzhou, China

    Yuhui Shi

  3. Automation College, Harbin Engineering University, 150001, Harbin, China

    Hongwei Mo

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© 2013 Springer-Verlag Berlin Heidelberg

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Wu, H., Sun, K. (2013). Improved Ant Colony Classification Algorithm Applied to Membership Classification. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38703-6_33

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  • DOI: https://doi.org/10.1007/978-3-642-38703-6_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38702-9

  • Online ISBN: 978-3-642-38703-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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