Machine-assisted learning: enhancing evolutionary rules extraction algorithm with expert knowledge

Abstract. Machine learning is a very important aspect for improving experts ’ everyday work. Not that it only helps in a routine task such as classification, where the state-of-the-art learning tools are a necessity; machine learning also helps in learning new patterns, which potentially reveal a whole new perspective onto areas not even imaginable so far. In order to squeeze as much out of machines as possible, an expert assistance in a learning process could be of vital importance to achieve the desirable results. In the paper we present a possible approach to enhance the machine learning algorithm with the help of experts ’ assessment of the progressing solution in order to find new patterns in the available data. We call the process machine-assisted learning. The method for automatic extraction of rules is presented that is based on the evolutionary induction of decision trees and automatic programming. The method is applied to a cardiovascular database, several sets of rules are induced upon different groups of attributes which should possibly reveal the presence of some specific cardiovascular problems in young patients. The obtained rules are assessed by physicians to evaluate the strength of the developed knowledge discovery method