On the use of genetic programming for mining comprehensible rules in subgroup discovery

This paper proposes a novel grammar-guided genetic programming algorithm for subgroup discovery. This algorithm, called comprehensible grammar-based algorithm for subgroup discovery (CGBA-SD), combines the requirements of discovering comprehensible rules with the ability to mine expressive and flexible solutions owing to the use of a context-free grammar. Each rule is represented as a derivation tree that shows a solution described using the language denoted by the grammar. The algorithm includes mechanisms to adapt the diversity of the population by self-adapting the probabilities of recombination and mutation. We compare the approach with existing evolutionary and classic subgroup discovery algorithms. CGBA-SD appears to be a very promising algorithm that discovers comprehensible subgroups and behaves better than other algorithms as measures by complexity, interest, and precision indicate. The results obtained were validated by means of a series of nonparametric tests.