The paper "Generating Accurate Rule Sets Without Global Optimization" by Eibe Frank and Ian H. Witten introduces a new algorithm called PART (for "Partial Decision Trees") that learns accurate rule sets without the need for global optimization. The authors compare PART to two dominant rule-learning schemes, C4.5 and RIPPER, which both involve a two-stage process: inducing an initial rule set and then refining it through complex optimization steps. In contrast, PART learns rules one at a time by repeatedly generating partial decision trees, combining the separate-and-conquer rule-learning technique with the creation of rules from decision trees. The key advantage of PART is its simplicity and efficiency. It avoids the complex and time-consuming global optimization processes of C4.5 and RIPPER, which can lead to over-pruning and reduced accuracy. PART builds partial decision trees, where some subtrees remain undefined, and extracts rules from these trees. This approach ensures that rules are only generalized once all implications are known, avoiding hasty generalization. The algorithm's runtime is efficient, and it performs well on standard datasets, producing rule sets that are as accurate as those generated by C4.5 and more accurate than those produced by RIPPER. Experimental results on 34 standard datasets from the UCI repository show that PART outperforms C4.5 on nine datasets, performs worse on six, and is significantly more accurate than RIPPER on fourteen datasets. The average size of the rule sets produced by PART is generally smaller than those of C4.5 and C5.0 but larger than RIPPER's. The paper concludes by suggesting future research directions, such as exploring stopping criteria based on the minimum description length principle or using reduced error pruning instead of pessimistic pruning.The paper "Generating Accurate Rule Sets Without Global Optimization" by Eibe Frank and Ian H. Witten introduces a new algorithm called PART (for "Partial Decision Trees") that learns accurate rule sets without the need for global optimization. The authors compare PART to two dominant rule-learning schemes, C4.5 and RIPPER, which both involve a two-stage process: inducing an initial rule set and then refining it through complex optimization steps. In contrast, PART learns rules one at a time by repeatedly generating partial decision trees, combining the separate-and-conquer rule-learning technique with the creation of rules from decision trees. The key advantage of PART is its simplicity and efficiency. It avoids the complex and time-consuming global optimization processes of C4.5 and RIPPER, which can lead to over-pruning and reduced accuracy. PART builds partial decision trees, where some subtrees remain undefined, and extracts rules from these trees. This approach ensures that rules are only generalized once all implications are known, avoiding hasty generalization. The algorithm's runtime is efficient, and it performs well on standard datasets, producing rule sets that are as accurate as those generated by C4.5 and more accurate than those produced by RIPPER. Experimental results on 34 standard datasets from the UCI repository show that PART outperforms C4.5 on nine datasets, performs worse on six, and is significantly more accurate than RIPPER on fourteen datasets. The average size of the rule sets produced by PART is generally smaller than those of C4.5 and C5.0 but larger than RIPPER's. The paper concludes by suggesting future research directions, such as exploring stopping criteria based on the minimum description length principle or using reduced error pruning instead of pessimistic pruning.