Evolutionary multi objective optimization for rule mining: a review

Abstract

Evolutionary multi objective optimization (EMOO) systems are evolutionary systems which are used for optimizing various measures of the evolving system. Rule mining has gained attention in the knowledge discovery literature. The problem of discovering rules with specific properties is treated as a multi objective optimization problem. The objectives to be optimized being the metrics like accuracy, comprehensibility, surprisingness, novelty to name a few. There are a variety of EMOO algorithms in the literature. The performance of these EMOO algorithms is influenced by various characteristics including evolutionary technique used, chromosome representation, parameters like population size, number of generations, crossover rate, mutation rate, stopping criteria, Reproduction operators used, objectives taken for optimization, the fitness function used, optimization strategy, the type of data, number of class attributes and the area of application. This study reviews EMOO systems taking the above criteria into consideration. There are other hybridization strategies like use of intelligent agents, fuzzification, meta data and meta heuristics, parallelization, interactiveness with the user, visualization, etc., which further enhance the performance and usability of the system. Genetic Algorithms (GAs) and Genetic Programming (GPs) are two widely used evolutionary strategies for rule knowledge discovery in Data mining. Thus the proposed study aims at studying the various characteristics of the EMOO systems taking into consideration the two evolutionary strategies of Genetic Algorithm and Genetic programming.

MOCA-I: Discovering Rules and Guiding Decision Maker in the Context of Partial Classification in Large and Imbalanced Datasets

“…The three objectives identified in the previous section highlight the need of methods able to deal with several objectives. Multi-objective optimization can handle such problems; Srinivasan and Ramkrishnan made a review of rule mining approaches using multi-objective optimization [13].…”

“…A neighborhood of a rule can be, for example, all rules having one more or one less term. Most of multi-objective rule mining contributions presented in the review of Srinivasan and Ramkrishnan are based on the metaheuristic NSGA-II (genetic algorithm dedicated to multi-objective problems) [13]. DMLS has previously proven to give at least as good results as NSGA-II on several problems [16].…”

Cultural Algorithm Toolkit for Interactive Knowledge Discovery

“…Some systems allow the user to specify the metrics for optimization and/or the threshold values for rule selection while a very few systems allow the user to interact with the system during execution [8]. Iglesia et al [6] [9], propose the use of multi-objective optimization evolutionary algorithms, to allow the user to interactively select a number of interest measures and deliver the best nuggets.…”

A hybrid agent based virtual organization for studying knowledge evolution in social systems

“…Ishibuchi [18] proposes an EMOA to the design of accurate and interpretable fuzzy rule-based systems and have applied multi-objective genetic fuzzy rule selection. A detailed discussion of evolutionary algorithms for Multi-Objective optimization of classification rules is found in [19] .…”