Aiming at the existing feature selection methods lacking the ability to measure the overlap/separation of different classes of data, an Interference Entropy of Two-Class Distinguishing (IET-CD) method was proposed to evaluate the two-class distinguishing ability of features. For the feature containing two classes (positive and negative), firstly, the mixed conditional probability of the negative class samples within the range of positive class data and the probability of the negative class samples belonging to the positive class were calculated; then, the confusion probability was calculated by the mixed conditional probability and attribution probability, and the confusion probability was used to calculate the positive interference entropy. In the similar way, the negative interference entropy was calculated. Finally, the sum of positive and negative interference entropies was taken as the two-class interference entropy of the feature. The interference entropy was used to evaluate the distinguishing ability of the feature to the two-class sample. The smaller the interference entropy value of the feature, the stronger the two-class distinguishing ability of the feature. On three UCI datasets and one simulated gene expression dataset, five optimal features were selected for each method, and the two-class distinguishing ability of the features were compared, so as to compare the performance of the methods. The experimental results show that the proposed method is equivalent or better than the NEFS （Neighborhood Entropy Feature Selection） method, and compared with the Single-indexed Neighborhood Entropy Feature Selection (SNEFS), feature selection based on Max-Relevance and Min-Redundancy (MRMR), Joint Mutual Information (JMI) and Relief method, the proposed method is better in most cases. The IET-CD method can effectively select features with better two-class distinguishing ability.

In recent years, test question resources in online education has grown at an explosive rate. It is difficult for students to find appropriate questions from the mass of question resources. Many test question recommendation methods for students have been proposed to solve this problem. However, many problems exist in traditional test question recommendation methods based on unified probalilistic matrix factorization; especially information of student knowledge points is not considered, resulting in low accuracy of recommendation results. Therefore, a kind of personalized test question recommendation method based on unified probalilistic matrix factorization was proposed. Firstly, through a cognitive diagnosis model, the student knowledge point mastery information was obtained. Secondly, the process of unified probalilistic matrix factorization was executed by combining the information of students, test questions and knowledge points. Finally, according to the difficulty range, the test questions were recommended. The experimental results show that the proposed method gets the best recommedation results in the aspect of accuracy of question recommendation for different range of difficulty, compared to other traditional recommendation methods, and has a good application prospect.

In the research of cognitive radio networks, the usual spectrum sensing techniques do not consider fusing the judgment results respectively according to different cognitive users. To solve this problem, a double-threshold cooperative spectrum sensing algorithm based on multi-fusion rule was proposed. Using the combination of AND-rule and OR-rule according to the different reliability of the judgment results of the cognitive users, all the judgmental results were fused respectively. The theoretical analysis and simulation results show that the proposed algorithm significantly improves the spectrum sensing performance for the cognitive radio networks as opposed to the conventional methods.