On Classifier Systems, Disintegrated Representation and Robust Data Mining

Abstract

Let x be a boolean vector of predictors and y a scalar response associated with it. Consider the problem of learning the relationship between predictors and response on the basis of a sample of observed pairs (x,y). Various proposed models for automated inference in this case are discussed, and some general dimensions for comparison among them are laid out. These dimensions are inspired by basic characteristics of (inductive) human learning. For example, our ability to receive, encode and modify knowledge in an unlimited number of ways would require a highly flexible representation, while our ability to detect and not be misguided by exceptional phenomena would entail some kind of uncertainty assessment. Systems that emulate such features of human learning may inherit some of its intrinsic robustness.

Classifier systems (CSs) provide a rich framework for general computation and learning, which are intimately related to cognitive and statistical modelling [6,7]. These systems combine low-level building blocks, called classifiers, according to broad plausible heuristics, to form emerging high-level knowledge structures. Knowledge is indeed distributed among classifiers, and these are subject to mild representational constraints. This is an example of disintegrated representation. It is argued that this kind of representation provides a highly flexible approach to automated inference. A new prototype based on a simple CS architecture introduces disintegrated representation in the above data analysis context.