To improve the performance of k-Nearest Neighbor (kNN) model on class-imbalanced data, a new kNN classification algorithm was proposed. Different from the traditional kNN, for the learning process, the majority set was partitioned into several clusters by using partitioning method (such as K-Means), then each cluster was merged with the minority set as a new training set to train a kNN model, therefore a classifier library was constructed consisting of serval kNN models. For the prediction, using a partitioning method (such as K-Means), a model was selected from the classifier library to predict the class category of a sample. By this way, it is guaranteed that the kNN model can efficiently discover local characteristics of the data, and also fully consider the effect of imbalance of the data on the performance of the classifier. Besides, the efficiency of kNN was also effectively promoted. To further enhance the performance of the proposed algorithm, Synthetic Minority Over-sampling TEchnique (SMOTE) was applied to the proposed algorithm. Experimental results on KEEL data sets show that the proposed algorithm effectively enhances the generalization performance of kNN method on evaluation measures of recall, g-mean, f-measure and Area Under ROC Curve (AUC) on majority set partitioned by random partition strategy, and it also shows great superiority to other state-of-the-art methods.

The Host Identity Protocol (HIP) macro mobility management was introduced into Distributed Mobility Management (DMM) architecture, and Rendezvous Server (RVS) was co-located with the DMM mobility access routing functionality in Distributed Access Gateway (D-GW). By extending the HIP protocol package header parameters, the HIP BEX messages carried host identifier tuple (HIT, IP address) to the D-GW new registered, and the new D-GW forwarded the IP address using the binding massage. Through the established tunnel, data cached in the front D-GW would be later loaded to the new D-GW. This paper proposed a handover mechanism to effectively ensure data integrity, and the simulation results show that this method can effectively reduce the total signaling overhead. Furthermore, the security of HIP-based mobility management can be guaranteed.

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.

Aiming at the problem of equivalent conversion from conjunctive normal form to disjunctive normal form, an effective algorithm was proposed to construct reduction tree based on discernibility matrix. The discernibility set was acquired by improving discernibility matrix, and then reduction tree was designed to describe concrete process of getting reductions, finally each path from root to leaf was set to a reduction. This method decreases overhead in forming and storing discernibility matrix, and simplifies the process of acquiring all reductions of decision system.