Abstract：Locally Weighted Naive Bayes (LWNB) is a good improvement of Naive Bayes (NB) and Discriminative Frequency Estimate (DFE) remarkably improves the generalization accuracy of Naive Bayes. Inspired by LWNB and DFE, this paper proposed Gradually Contracting Spaces (GCS) algorithm to learn parameters of Naive Bayes. Given a test instance, GCS found a series of subspaces in global space which contained all training instances. All of these subspaces contained the test instance and any of them must be contained by others that are bigger than it. Then GCS used training instances contained in those subspaces to gradually learn parameters of Naive Bayes (NB) by Modified version of DFE (MDFE) which was a modified version of DFE and used NB to classify test instances. GSC trained Naive Bayes with all training data and achieved an eager version, which was the essential difference between GSC and LWNB. Decision tree version of GCS named GCS-T was implemented in this paper. The experimental results show that GCS-T has higher generalization accuracy compared with C4.5 and some Bayesian classification algorithms such as Naive Bayes, BaysianNet, NBTree, Hidden Naive Bayes (HNB), LWNB, and the classification speed of GCS-T is remarkably faster than LWNB.
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