Using k-Interactive Measure in Optimization-Based Data Mining

Optimization-based methods have been used for data separation in different domains and applications since 1960s. The commonality of those methods is to separate data by minimizing the overlapping between the groups and regard contribution from all the attributes toward the target of classification is the sum of every single attribute. However, the interaction among the attributes in the data is not considered at all. The theory of non-additive measures is used to describe those interactions. The consideration of the interactions is a breakthrough for dealing with the nonlinearity of data. Through the non-additive measure has been successfully utilized in optimization-based classification, it increases the computation cost as well as the quadratic programming models particularly designed for dealing with the nonlinearity. In this paper, we proposed the optimization-based classification method with the signed k-interactive measure. The experimental results shows that it successfully reduced the computation but retained the classification power.