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ROPAC: Rule OPtimized Aggregation Classifier
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AI
DOI：10.1016/j.eswa.2024.123897.png)
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摘要
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In the era of data -driven decision -making, extracting meaningful insights from vast amounts of information is paramount. In organizing this data, classification methods play a pivotal role. Among the existing classification techniques, rule -based classifiers have gained prominence for their effectiveness and interpretability. One such is the R ule A ggregation C lassifi ER (RACER), known for its exceptional performance but limited when dealing with high -dimensional, low -sample -size datasets. In this paper, we introduce the R ule OP timized A ggregation C lassifier (ROPAC) as an extension of RACER that incorporates two different rule optimization methods, resulting in ROPAC-L and ROPAC-M, which aim to improve overall performance and classification accuracy. We evaluated this algorithm through experimentation with fifty datasets from reputable sources, such as the OpenML website and the UCI Machine Learning Repository. Furthermore, the proposed algorithm's accuracy is compared with fifteen well-known classifiers. Our results demonstrate that ROPAC outperforms all the other algorithms in terms of accuracy, showcasing its superiority and dominance in various data scenarios.
Keyword:
Rule OPtimized Aggregation Classifier (ROPAC)
Rule Aggregation ClassifiER (RACER)
Rule-based classifier
Data classification
Data mining
期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
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A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning基于遗传规则选择和横向调整的高维问题模糊关联规则分类模型
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