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CFAR++: Enhancing Rule Based Classifier

Published: 15 March 2024 Publication History

Abstract

Over the last few years, associative classifiers have shown massive success in mining patterns using association rules. These rule-based classifiers offer a level of human interpretability, addressing a common concern stemming from several deep learning models. Various associative classifiers have been proposed over the past that have shown state-of-the-art performance. However, those classifiers suffer the limitation of requiring parametric values which vary across different datasets. Furthermore, those frameworks do not consider the statistical significance of the rules. Recently, some works have addressed this limitation by proposing an associative classifier that incorporates the idea of using statistical significance to mine association classification rules. Though the recent associative classifiers show good performance, their performance is greatly affected by the dimension of the data. In this study, we explore the weakness of the recent associative classification models and experiment with using ensemble models to overcome such limitations, particularly on aggregating the ensemble models in a concise but effective predictor. We use 10 UCI datasets for evaluation of our new approach. From our study, we find the results based on the ensemble model with a delayed pruning are very competitive and can better handle large dimensional data spaces.

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cover image ACM Conferences
ASONAM '23: Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
November 2023
835 pages
ISBN:9798400704093
DOI:10.1145/3625007
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 15 March 2024

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  1. rule based classification
  2. ensemble model
  3. interpretable model

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ASONAM '23 Paper Acceptance Rate 53 of 145 submissions, 37%;
Overall Acceptance Rate 116 of 549 submissions, 21%

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