arXiv：cs.LG（机器学习，全量分类）· Ali Alsalama, Ahmed Kubba, Ghaith Jamjoum, Zaher Al Aghbari·· 14 小时前AI 评分21
基于关联规则算法的乳腺癌分类方法
Classification Based on Association Rules Algorithm for Breast Cancer
AI 导读
该论文提出一种基于加权分类的关联规则数据挖掘技术，用于乳腺癌分类，包含规则生成、规则剪枝和规则预测三个核心算法。规则剪枝依据特定标准剔除规则并按对训练数据的影响分为主要和次要两组，规则预测则将剪枝后的规则应用于测试数据分类。该方法在多个测试样本上验证了可行性与性能。
正文
Abstract:Breast cancer is a significant contributor to female mortality across the world, displaying one of the highest oc currence rates among the various cancer types. In response to the need for early breast cancer detection, researchers have increasingly turned to association rule-based classification as a favored method. Association Rule mining is a data mining approach which offers the benefit of yielding results that are readily understandable for medical professionals. This paper introduces a novel association rule-based data mining technique for breast cancer classification based on a weighted classification approach. This implementation employs three core algorithms: Rule Generation, Rule Pruning, and Rule Prediction. Rule Generation identifies frequent itemsets and creates association rules. Rule Pruning eliminates rules using specific criteria and separates them into major and minor groups based on their influence on training data. Rule Prediction applies the pruned rules to classify test data. The final prediction algorithm was tested on several testing samples to show the feasibility and performance of the approach.
| Comments: | 6 pages, 1 figure, 1 table, accepted & presented at Advances in Science and Engineering Technology International Conferences (ASET) 2024 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.00174 [cs.LG] |
| (or arXiv:2610.00174v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00174 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | 2024 Advances in Science and Engineering Technology International Conferences (ASET), Abu Dhabi, United Arab Emirates, 2024, pp. 1-6 |
| Related DOI: | https://doi.org/10.1109/ASET60340.2024.10708711
DOI(s) linking to related resources |
Submission history
From: Ahmed Ammar [view email]
[v1]
Wed, 16 Sep 2026 11:39:16 UTC (1,981 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org