Article

An associative classifier based on positive and negative rules

Authors:

Maria-Luiza Antonie,

Osmar R. ZaïaneAuthors Info & Claims

DMKD '04: Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery

Pages 64 - 69

https://doi.org/10.1145/1008694.1008705

Published: 13 June 2004 Publication History

Abstract

Associative classifiers use association rules to associate attribute values with observed class labels. This model has been recently introduced in the literature and shows good promise. The proposals so far have only concentrated on, and differ only in the way rules are ranked and selected in the model. We propose a new framework that uses different types of association rules, positive and negative. Negative association rules of interest are rules that either associate negations of attribute values to classes or negatively associate attribute values to classes. In this paper we propose a new algorithm to discover at the same time positive and negative association rules. We introduce a new associative classifier that takes advantage of these two types of rules. Moreover, we present a new way to prune irrelevant classification rules using a correlation coefficient without jeopardizing the accuracy of our associative classifier model. Our preliminary results with UCI datasets are very encouraging.

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Cited By

Costa GForestiero AOrtale R(2023)Rule-Based Detection of Anomalous Patterns in Device Behavior for Explainable IoT SecurityIEEE Transactions on Services Computing10.1109/TSC.2023.332782216:6(4514-4525)Online publication date: Nov-2023
https://doi.org/10.1109/TSC.2023.3327822
Dick S(2022) On Complex Fuzzy S -Implications IEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2020.30381606:2(409-415)Online publication date: Apr-2022
https://doi.org/10.1109/TETCI.2020.3038160
Sood NBindra LZaiane O(2020)Bi-Level Associative Classifier Using Automatic Learning on RulesDatabase and Expert Systems Applications10.1007/978-3-030-59003-1_14(201-216)Online publication date: 14-Sep-2020
https://doi.org/10.1007/978-3-030-59003-1_14
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An associative classifier based on positive and negative rules
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
2. Information systems
  1. Information systems applications

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cover image ACM Conferences

DMKD '04: Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery

June 2004

85 pages

ISBN:158113908X

DOI:10.1145/1008694

Program Chairs:
Gautam Das
Microsoft Research
,
Bing Liu
University of Illinois at Chicago
,
Philip S. Yu
IBM T.J. Watson Research Center

Copyright © 2004 ACM.

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Published: 13 June 2004

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DMKD04: 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery 2004

13 06 2004

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Cited By

Costa GForestiero AOrtale R(2023)Rule-Based Detection of Anomalous Patterns in Device Behavior for Explainable IoT SecurityIEEE Transactions on Services Computing10.1109/TSC.2023.332782216:6(4514-4525)Online publication date: Nov-2023
https://doi.org/10.1109/TSC.2023.3327822
Dick S(2022) On Complex Fuzzy S -Implications IEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2020.30381606:2(409-415)Online publication date: Apr-2022
https://doi.org/10.1109/TETCI.2020.3038160
Sood NBindra LZaiane O(2020)Bi-Level Associative Classifier Using Automatic Learning on RulesDatabase and Expert Systems Applications10.1007/978-3-030-59003-1_14(201-216)Online publication date: 14-Sep-2020
https://doi.org/10.1007/978-3-030-59003-1_14
Looi WDhaliwal MAlhajj RRokne J(2019) Recommender System for Items in Dota 2 IEEE Transactions on Games10.1109/TG.2018.284412111:4(396-404)Online publication date: Dec-2019
https://doi.org/10.1109/TG.2018.2844121
Christopher J(2019)The Science of Rule-based Classifiers2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/CONFLUENCE.2019.8776954(299-303)Online publication date: Jan-2019
https://doi.org/10.1109/CONFLUENCE.2019.8776954
Martínez-Romero MO'Connor MEgyedi AWillrett DHardi JGraybeal JMusen M(2019)Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databasesDatabase10.1093/database/baz0592019Online publication date: 10-Jun-2019
https://doi.org/10.1093/database/baz059
Dick SSussner P(2019)Integrating Antonyms in Fuzzy Inferential Systems via Anti-membershipFuzzy Techniques: Theory and Applications10.1007/978-3-030-21920-8_22(237-245)Online publication date: 11-Jun-2019
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Do Van TDuc HNguyen G(2018)Classify high dimensional datasets using discriminant positive negative association rules2018 5th Asian Conference on Defense Technology (ACDT)10.1109/ACDT.2018.8592973(1-7)Online publication date: Oct-2018
https://doi.org/10.1109/ACDT.2018.8592973
Liu YLi YYang JRen YSun GLi Q(2018)An Improved Apriori Algorithm Based on Matrix and Double Correlation Profit ConstraintData Science10.1007/978-981-13-2203-7_27(359-370)Online publication date: 9-Sep-2018
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Li JZaiane O(2017)Exploiting statistically significant dependent rules for associative classificationIntelligent Data Analysis10.3233/IDA-16314121:5(1155-1172)Online publication date: 10-Oct-2017
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