In this chapter, we propose a new method to measure the importance of input variables and to examine the effect of the input variables on other components. We applied the method to competitive learning, in particular, self-organizing maps, to demonstrate the performance of our method. Because our method is based upon our information-theoretic competitive learning, it is easy to incorporate the ...

A competition which is based on the results of (partial) pairwise comparisons can be modelled by means of a directed graph. Given initial weights on the nodes in such digraph competitions, we view the measurement of the importance (i.e., the cardinal ranking) of the nodes as an allocation problem where we redistribute the initial weights on the basis of insights from cooperative game theory. Af...

Objective: Determining which subgroups show the most substantial differences on a measure is a common use of surveys. How to accurately and fairly determine which subgrouping is most important has not been addressed adequately in the literature. I show how dominance analysis is a useful way to identify the most important subgroup differences. Because surveys commonly employ complex sampling des...

In the cloud right weight attribute encryption scheme based on multi-agency. Under existing cloud computing environment based on multi-agency access control scheme generally do not take into account the weights of the attributes that the status attributes are equal. But in real life, with the right to property values are meaningful. Each attribute in the system that served as different roles, t...

Previous work on feature weighting for case-based learning algorithms has tended to use either global weights or weights that vary over extremely local regions of the case space. This paper examines the use of coarsely local weighting schemes, where feature weights are allowed to vary but are identical for groups or clusters of cases. We present a new technique, called class distribution weight...

Effective utilization of heterogeneous multi-modal data for Alzheimer's Disease (AD) diagnosis and prognosis has always been hampered by incomplete data. One method to deal with this is low-rank matrix completion (LRMC), which simultaneous imputes missing data features and target values of interest. Although LRMC yields reasonable results, it implicitly weights features from all the modalities ...

Data envelopment analysis (DEA) is a technique for performance evaluation of peer decision making units (DMUs). The network DEA models study the internal structures of DMUs. Using two-stage network structures as an example, the current paper examines additive efficiency decomposition where the overall efficiency is defined as a weighted average of stage efficiencies and the weights are used to ...