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We address the problem of nding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the deenitions used in the machine learning literature do not adequately partition the features into useful categories of relevance. We present deeni-tions(More)
We present MLC ++ , a library of C ++ classes and tools for supervised Machine Learning. While MLC ++ provides general learning algorithms that can be used by end users, the main objective is to provide researchers and experts with a wide variety of tools that can accelerate algorithm development, increase software reliability, provide comparison tools, and(More)
In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To a c hieve the best possible performance with a particular learning algorithm on a particular training set, a feature subset selection method should consider how the(More)