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Offered an applicant part group Utes, the particular feature Ci(Ci Utes) will be approximated. Permit IDSi are the variety of features that fall into interdependent partnership with all the characteristic Ci. The share with the attribute Ci on group Ersus may be changed because following description: ��i(Ersus)Equals{1??NMI��(S;D?�O?Ci)��0,IDSi��|S|20else (14) which means that the feature is crucial to win the coalition only if it both increases the relevance of the unitary subset S on the target class and is interdependent with at PF-477736 mouse least half of the members. Furthermore, we can get the average contribution of player i to all coalitions according to the Banzhaf value. The definition about ��i(S) is similar as the formula (11) in [8]. Nevertheless, we use neighborhood conditional mutual information NMI��(S; D�OCi) rather than Shannon's conditional mutual information in [8]. The neighborhood entropy-based method can avoid discretization of the samples. The traditional feature selection methods were proposed based on some feature measures Trimebutine [2, 6, 13], such as mutual information (MI), rough set (RS), and mRMR. These measures were usually used in evaluating the significance of features. Actually, this type of significance can only be called the significance for decision (SIGFD). In the framework of neighborhood entropy-based cooperative game theory (NECGT), the contribution of one feature in the coalitions is another important aspect. Here, we give the neighborhood PIK-75 chemical structure entropy-based formulaic feature measure according to [9]: SIG(i)=?B(i)��SIGFD(i), (15) where SIGFD(i) can be any of traditional feature measures and ?B(i) is the Banzhaf value. 4. Feature Selection Algorithm with NECGT Before giving the algorithm of feature selection, details of the feature contribution evaluation method based on the Banzhaf value are presented in Algorithm 1. Algorithm 1 Feature contribution evaluation based on the Banzhaf value. An information system is IS = (U, C, D), where U is a nonempty finite set of objects, C is a nonempty finite set of conditional attributes, and D is the decision attribute which represents the target classes. The output of this evaluation framework is a vector �� of which each element ��(i) represents the Banzhaf value ?B(i) of feature Ci. In fact, it is impractical to get the optimal subset of features from 2n ? 1 candidates through exhaustive search, where n is the number of features. The greedy search guided by some heuristics is usually more efficient than the plain brute-force exhaustive search. A forward search algorithm for feature selection with NECGT is written as shown in Algorithm 2. Algorithm 2 Feature selection with NECGT. In the forward greedy search, one starts with an empty set of attributes and keeps adding features to the subset of selected attributes one by one. Each selected attribute maximizes the significance of the current subset.