A Dynamic Committee Scheme on Multiple-Criteria Linear Programming Classification Method

Abstract

Abstract. This paper aims to provide a scheme for effectively and efficiently finding an approximately optimal example size with respect to a given dataset when using Multiple-Criteria Linear Programming (MCLP) classification method. By integrating techniques of both progressive sampling and classification committee, it designs a dynamic classification committee scheme for MCLP. The experimental results have shown that our idea is feasible and the scheme is effective and efficient for exploring an approximately optimal sample size. The empirical results also help us to further investigate some general specialties of MCLP, such as the more general function expressions reflecting the relationship between accuracy and sample size, and between computing cost and sample size.

Affected States Soft Independent Modeling by Class Analogy from the Relation Between Independent Variables, Number of Independent Variables and Sample Size

“…A similar situation can be observed if the number of the independent variable is 100. In this line, there are classification methods in the literature indicating the decrease of accuracy results through the increase of the size of the sample (13).…”