By Memon Wong
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Moreover we introduce a new criterion for splitting the features based on maximizing the strength of the views and their diversity to take advantage of the co-training paradigm. As follows we describe our proposed measures in more details. 1 Confidence of the Views The ﬁrst requirement for successful co-training is that the features are redundant enough, that is each view is strong enough to perform classiﬁcation on its own. Based on that hypothesis we propose a genetic algorithm to select the split that maximizes the strength of the views.
E. so that it is not necessarily the case that feature sets within the individual classiﬁers are fully coincident) then it can be shown that linear classiﬁer combination (eg Sum Rule, Product Rule) is either equivalent to, or bounded by, back-projection, the inverse opera1 M Σi=1 fi (xi , y). However, this introduces tion to Radon projection; pb (X n ) = M n an axially aligned artefact, A(X ) = Σi dxi . all dX ni , that is a consequence of the fact that the Radon projections induced by feature selection represent only 46 D.
In this case, each feature set is suﬃcient to perform classiﬁcation and the views are truly independent. For example in an email spam classiﬁcation problem, one view may contain the features describing the subject of the email and the other may contain the features describing the body. Natural splits satisfy the co-training requirements proposed by Blum and Mitchell, they showed that using unlabeled data for co-training improves the performance when a natural split exists. New Feature Splitting Criteria for Co-training 25 Input: – L: a small set of labeled example – U: a large set of unlabeled example – V1, V2: two sets of describing the example Algorithm: – – – – – – – – – Create a pool U’ by randomly choosing u examples from U Loop for k iterations Train Classiﬁer C1 from L based on V1 Train Classiﬁer C2 from L based on V2 C1 predicts the class of examples from U’ based on V1 and chooses the most conﬁdently predicted p positive and n negative examples E1 C2 predicts the class of examples from U’ based on V2 and chooses the most conﬁdently predicted p positive and n negative examples E2 E1 and E2 are removed from U’ and added with their labels to L Randomly chose 2p+2n examples from U to replenish U’ End Fig.