Chinese Journal of Computers 

About: Chinese Journal of Computers is an academic journal. The journal publishes majorly in the area(s): Web service & Software. Over the lifetime, 3606 publications have been published receiving 21470 citations.

Decision Table Reduction based on Conditional Information Entropy

Abstract: This paper analyzes the information view of rough set theory and compares it with the algebra view of rough set theory Some equivalence relations and other kind of relations like inclusion relation between the information view and the algebra view of rough set theory are resulted through comparing each other Two novel heuristic knowledge reduction algorithms are developed based on conditional information entropy, that is, conditional entropy based algorithm for reduction of knowledge with computing core (CEBARKCC) and conditional entropy based algorithm for reduction of knowledge without computing core (CEBARKNC) These two algorithms are compared with a mutual information based algorithm for reduction of knowledge (MIBARK) of Duoqian Miao through theoretical analysis and experimental simulation CEBARKCC algorithm and CEBARKNC algorithm have good performance in simulation

Big Data Security and Privacy Protection

Abstract: Nowadays big data has become a hot topic in both the academic and the industrial research.It is regarded as a revolution that will transform how we live,work and think.However, there are many security risks in the field of data security and privacy protection when collecting, storing and utilizing big data.Privacy issues related with big data analysis spell trouble for individuals.And deceptive or fake information within big data may lead to incorrect analysis results. This paper summarizes and analyzes the security challenges brought by big data,and then describes the key technologies which can be exploited to deal with these challenges.Finally,this paper argues that big data brings not only challenges,but also technical revolution in the field of information security.

Neural Network Ensemble

Abstract: Neural network ensemble can significantly improve the generalization ability of learning systems through training a finite number of neural networks and then combining their results. It is not only helpful for scientists to investigate machine learning and neural computing but also helpful for common engineers to solve real world problems using neural network techniques. Therefore neural network ensemble has been regarded as an engineering neural computing technology that has great application prospect. Also it has become a hot topic in both machine learning and neural computing communities. In this paper, the state of the art of neural network ensemble is surveyed from three aspects including implementation methods, theoretical analysis, and applications. Moreover, some issues valuable for future exploration in this area are indicated and discussed.

A Quick Attribute Reduction Algorithm with Complexity of max(O(｜C｜｜U｜),O(｜C｜~2｜U/C｜))

Abstract: Computing U/C is one of the most important and time-consuming computation in attribute reduction based on positive region.At present,the idea of the best algorithm for computing U/C is based on quick sorting,and it's time complexity is O(｜C｜｜U｜log｜U｜).In this paper,a new algorithm based on radix sorting for computing U/C is provided,and its complexity is cut down to O(｜C｜｜U｜).On the other hand,it is not fully reasonable to regard approximate quality as heuristic information in attribution reduction algorithm based on positive region.So a new and relatively reasonable formula measuring attribution importance is designed for reducing searching space as quickly as possible,and the recursive calculating method of the formula is provided.The algorithm complexity of calculating the formula is descended to O(｜C-P｜｜U′-U′_(P)｜).Then the formula measuring attribute importance is used as heuristic information to design an efficient attribute reduction algorithm,whose worst time complexity is cut down to(max)(O(｜C｜｜U｜,O(｜C｜~(2)｜U/C｜)).An example is used to illustrate the efficiency of the new algorithm. At last,experimental result shows that the new algorithm is not only efficient but also scalable.