Explorations on Improving Interpretability of Decision Making Processes of Rule-Based Classifiers

Abstract

Rule-based classifiers are often preferred over other types of learners due to the transparent mode in which decisions are made. Each decision rule includes in its premise conditions on attributes. When they are satisfied, the conclusion part of the rule comes into play and leads to assigning an object to a specific class. Following the classification process is relatively straightforward but can become more complex when the cardinality of rule set is high. Furthermore, when rules are induced from continuous data, the conditions listed belong to this domain as well, which makes them less general. This paper presents an illustrative example for the exploratory research methodology where the sets of rules are induced in the continuous input domain, but next, they are transformed by discretisation procedures, which results in a simplified representation of the data and knowledge patterns learnt. In addition, the rule sets are also filtered based on rankings obtained for variants of the transformed data. The processing results in reduced decision algorithms with categorical conditions. This simplification is advantageous in and of itself, but the experiments carried out on datasets in the stylometric domain show that it can also lead to enhanced performance of rule-based classifiers.