On Irrelevance of Attributes in Flexible Prediction

TL;DR

This paper investigates how attribute relevance affects the formation of conceptual hierarchies in flexible prediction, revealing that both weakly and strongly related attributes can impair classification quality and proposing methods to improve hierarchy construction.

Contribution

It provides an analysis of attribute relevance in flexible prediction, highlighting the effects of attribute selection, scaling, and correlation on conceptual hierarchy quality, and suggests strategies for better classification.

Findings

Attributes weakly and strongly related can impair classification

Proper attribute construction and scaling influence hierarchy quality

Breaking attribute symmetry improves classification

Abstract

This paper analyses properties of conceptual hierarchy obtained via incremental concept formation method called "flexible prediction" in order to determine what kind of "relevance" of participating attributes may be requested for meaningful conceptual hierarchy. The impact of selection of simple and combined attributes, of scaling and of distribution of individual attributes and of correlation strengths among them is investigated. Paradoxically, both: attributes weakly and strongly related with other attributes have deteriorating impact onto the overall classification. Proper construction of derived attributes as well as selection of scaling of individual attributes strongly influences the obtained concept hierarchy. Attribute density of distribution seems to influence the classification weakly It seems also, that concept hierarchies (taxonomies) reflect a compromise between the data and our interests in some objective truth about the data. To obtain classifications more suitable for one's purposes, breaking the symmetry among attributes (by dividing them into dependent and independent and applying differing evaluation formulas for their contribution) is suggested. Both continuous and discrete variables are considered. Some methodologies for the former are considered.