Rule Extraction on Numeric Datasets Using Hyper-rectangles
TL;DR: A new classification strategy is presented that uses hyper-rectangles as data descriptors to achieve a model that allows extracting knowledge in the form of classification rules and achieves the same accuracy level and number of extracted rules.
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Abstract: When there is a need to understand the data stored in a database, one of the main requirements is being able to extract knowledge in the form of rules Classification strategies allow extracting rules almost naturally In this paper, a new classification strategy is presented that uses hyper-rectangles as data descriptors to achieve a model that allows extracting knowledge in the form of classification rules The participation of an expert for training the model is discussed Finally, the results obtained using the databases from the UCI repository are presented and compared with other existing classification models, showing that the algorithm presented requires less computational resources and achieves the same accuracy level and number of extracted rules
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Citations
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References
Decompositional Rule Extraction from Support Vector Machines by Active Learning
TL;DR: A new active learning-based approach (ALBA) to extract comprehensible rules from opaque SVM models by explicitly making use of key concepts of the SVM: the support vectors, and the observation that these are typically close to the decision boundary.
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A hybrid PSO/ACO algorithm for discovering classification rules in data mining
TL;DR: The results show that PSO/ACO2 is very competitive in terms of accuracy to PART and thatPSO/ ACO2 produces significantly simpler (smaller) rule sets, a desirable result in data mining--where the goal is to discover knowledge that is not only accurate but also comprehensible to the user.
Mining Projected Clusters in High-Dimensional Spaces
Mohamed Bouguessa,Shengrui Wang +1 more
TL;DR: This work proposes a robust partitional distance-based projected clustering algorithm capable of detecting projected clusters of low dimensionality embedded in a high-dimensional space and avoids the computation of the distance in the full- dimensional space.
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Ensemble Rough Hypercuboid Approach for Classifying Cancers
TL;DR: Experimental results on some open cancer gene expression data sets show that the proposed method is capable of generating accurate and interpretable rules compared with some other machine learning methods, and is a feasible way of classifying cancer tissues in biomedical applications.
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