Weighted pattern trees: A case study with customer satisfaction dataset

Zhiheng Huang*, Masoud Nikravesh, Ben Azvine, Tamás D. Gedeon

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    3 Citations (Scopus)

    Abstract

    A pattern tree [1] is a tree which propagates fuzzy terms using different fuzzy aggregations. Each pattern tree represents a structure for an output class in the sense that how the fuzzy terms aggregate to predict such a class. Unlike decision trees, pattern trees explicitly make use of t-norms (i.e., AND) and t-conorms (OR) to build trees, which is essential for applications requiring rules connected with t-conorms explicitly. Pattern trees can not only obtain high accuracy rates in classification applications, but also be robust to over-fitting. This paper further extends pattern trees approach by assigning certain weights to different trees, to reflect the nature that different trees may have different confidences. The concept of weighted pattern trees is important as it offers an option to trade off the complexity and performance of trees. In addition, it enhances the semantic meaning of pattern trees. The experiments on British Telecom (BT) customer satisfaction dataset show that weighted pattern trees can slightly outperform pattern trees, and both of them are slightly better than fuzzy decision trees in terms of prediction accuracy. In addition, the experiments show that (weighted) pattern trees are robust to over-fitting. Finally, a limitation of pattern trees as revealed via BT dataset analysis is discussed and the research direction is outlined.

    Original languageEnglish
    Title of host publicationFoundations of Fuzzy Logic and Soft Computing - 12th International Fuzzy Systems Association World Congress, IFSA 2007, Proceedings
    PublisherSpringer Verlag
    Pages395-406
    Number of pages12
    ISBN (Print)9783540729174
    DOIs
    Publication statusPublished - 2007
    Event12th International Fuzzy Systems Association World Congress, IFSA 2007 - Cancun, Mexico
    Duration: 18 Jun 200721 Jun 2007

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume4529 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference12th International Fuzzy Systems Association World Congress, IFSA 2007
    Country/TerritoryMexico
    CityCancun
    Period18/06/0721/06/07

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