Communications of the ACM
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
A Further Comparison of Splitting Rules for Decision-Tree Induction
Machine Learning
Digital logic circuit analysis and design
Digital logic circuit analysis and design
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
C4.5: Programs for Machine Learning
C4.5: Programs for Machine Learning
Machine Learning
Machine Learning
On the Power of Decision Lists
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Test feature classifiers: performance and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computers in Biology and Medicine
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In most learning models, the induction methods that are learnable have low expressive power. The learnability of such methods are proved for some concept classes by assuming that the hypothesis space of the method contains the target concept. However, in real-world practical problems the type of target concept being dealt with is almost always unknown. In medical diagnosis, where mistakes can cause fatal results, it is very important to achieve high recognition rates and the use of more expressive methods are common. However, conventional methods are weak at handling irrelevant information which often appears in medical databases. In this paper, we consider test feature classifiers recently introduced by (Lashkia and Aleshin, 2001. IEEE Trans. Syst. Man Cybern. 31 (4), 643-650), and show that they meet all essential requirements to be of practical use in medical decision making, which are: ability to handle irrelevant attributes, high expressive power, high recognition ability, and ability to generate decisions by a set of rules.