Pairwise comparison based multiattribute decision-making (MADM) methods are widely used and studied in recent years. However, the perception and cognition towards the semantic representation for the linguistic rating scale and the way in which the pairwise comparisons are executed are still open to discuss. The commonly used ratio scale is likely to produce misapplications and the matrix based comparison style needs too many comparisons and is not able to guarantee the consistency of the matrix when the number of objects involved is large. This research proposes a new MADM method CBWM (Cognitive Best Worst Method) which adopts interval scale to represent the pairwise difference and only compares each object to the best object and the worst object rather than all the other objects. CBWM is a vector based method which only needs 2n-3 pairwise comparisons and is more likely to generate consistent comparisons and reliable results. The theoretical analysis and a real world application demonstrate the effectiveness of CBWM.

No abstract

This paper investigates the consistency definition and the weight-deriving method for additive interval fuzzy preference relations (IFPRs) using a particular characterization based on logarithms. In a recently published paper, a new approach with a parameter is developed to obtain priority weights from fuzzy preference relations (FPRs), then a new consistency definition for the additive IFPRs is defined, and finally linear programming models for deriving interval weights from consistent and inconsistent IFPRs are proposed. However, the discussion of the parameter value is not adequate and the weights obtained by the linear models for inconsistent IFPRs are dependent on alternative labels and not robust to permutations of the decision makers’ judgments. In this paper, we first investigate the value of the parameter more thoroughly and give the closed form solution for the parameter. Then, we design a numerical example to illustrate the drawback of the linear models. Finally, we construct a linear model to derive interval weights from IFPRs based on the additive transitivity based consistency definition. To demonstrate the effectiveness of our proposed method, we compare our method to the existing method on three numerical examples. The results show that our method performs better on both consistent and inconsistent IFPRs.

Abstract.The support vector machine model is based on the network security situation has strong randomness, is affected by many factors, and the number of types of network security incidents is uncertain, the reference sample is small, the prediction model of need "intelligent", according to SVM forecast algorithm. In order to select the parameters of SVM, genetic algorithm is introduced into the parameter selection in support vector machine, genetic algorithm optimization based on support vector machine structure (GA -SVM) situation awareness prediction model and method to measure data dimensionality reduction using principal component analysis, through simulation experiments, it is proved that this model is the prediction higher precision than neural network, classification and regression tree and cluster analysis prediction model.

Mutual information based feature selection algorithms are very popular currently. Though they perform well in many cases, they suffer from two drawbacks: (1) the neglect of feature interaction; (2) the overestimation of some features. To overcome these shortcomings, a new feature evaluation criterion considering feature interaction is proposed and a pruning rule is designed. Based on the criterion and pruning rule, a new feature selection algorithm WJMI is proposed. Experiments carried out on UCI real world dataset against other four algorithms demonstrate the effectiveness of WJMI.