C4.5: programs for machine learning
C4.5: programs for machine learning
Variable precision rough set model
Journal of Computer and System Sciences
Machine Learning
A computational TW3 classifier for skeletal maturity assessment: a computing with words approach
Journal of Biomedical Informatics
Dominance-based rough set approach to incomplete interval-valued information system
Data & Knowledge Engineering
A DIAMOND method of inducing classification rules for biological data
Computers in Biology and Medicine
A DIAMOND method for classifying biological data
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
International Journal of Applied Metaheuristic Computing
A note on hyper ellipse method for classifying biological and medical data
Computers in Biology and Medicine
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To induce critical classification rules from observed data is a major task in biological and medical research. A classification rule is considered to be useful if it is optimal and simultaneously satisfies three criteria: is highly accurate, has a high rate of support, and is highly compact. However, current classification methods, such as rough set theory, neural networks, ID3, etc., may only induce feasible rules instead of optimal rules. In addition, the rules found by current methods may only satisfy one of the three criteria. This study proposes a multi-criteria model to induce optimal classification rules with better rates of accuracy, support and compactness. A linear multi-objective programming model for inducing classification rules is formulated. Two practical data sets, one of HSV patients results and another of European barn swallows, are tested. The results illustrate that the proposed method can induce better rules than current methods.