A gene expression programming algorithm for discovering classification rules in the multi-objective space

Abstract

Multi-objective evolutionary algorithms have been criticized when they are applied to classification rule mining, and, more specifically, in the optimization of more than two objectives due to their computational complexity. It is known that a multi-objective space is much richer to be explored than a single-objective space. In consequence, there are only few multi-objective algorithms for classification and their empirical assessed is quite limited. On the other hand, gene expression programming has emerged as an alternative to carry out the evolutionary process at genotypic level in a really efficient way. This paper introduces a new multi-objective algorithm for discovering classification rules, AR-NSGEP (Adaptive Reference point based Non-dominated Sorting with Gene Expression Programming). It is a multi-objective evolution of a previous single-objective algorithm. In AR-NSGEP, the multi-objective search was based on the wellknown R-NSGA-II algorithm, replacing GA with GEP technology. Four objectives led the rules-discovery process, three of them (sensitivity, specificity and precision) were focused on promoting accuracy and the fourth (simpleness) on the interpretability of rules. AR-NSGEP was evaluated on several benchmark data sets and compared against six rule-based classifiers widely used. The AR-NSGEP, with four-objectives, achieved a significant improvement of the AUC metric with espect to most of the algorithms assessed, while the predictive accuracy and number of rules in the obtained models reached to acceptable results.

Support Vector Machine Chains with a Novel Tournament Voting

“…In some studies, genetic algorithm and gene expression programming (GEP) methods were used for the classification task, which offered the opportunity to evaluate our method from different perspectives. For instance, on the ionosphere dataset, SVMC (94.60%) demonstrated a stronger classification ability over the adaptive reference-point-based non-dominated sorting with GEP (AR-NSGEP) method (87.35%) [30], generational genetic algorithm (GGA) (90.85%), steady state genetic algorithm (SSGA) (91.04%), and crossgenerational elitist selection (CHC) method (90.63%) [31]. According to Table 4, the overall average accuracy reported in the state-of-the-art studies on the seven datasets was 85.68%, while the proposed method obtained average accuracy of 92.56%.…”

A Two-Stage Multi-objective Programming Model to Improve the Reliability of Solution

“…Because many practical decision-making problems can be formulated as a multi-objective programming, the wide application spectrum and ever-increasing strong interests have encouraged the substantial development of reliable models and efficient algorithms during the recent decades [14][15][16][17]. Numerous researches have been devoted to multi-objective programming combining with different theories and methods, and many significant theoretical and applied findings have been also achieved.…”

“…Numerous researches have been devoted to multi-objective programming combining with different theories and methods, and many significant theoretical and applied findings have been also achieved. Han et al [16,17] discussed the solution to multi-objective programming with hierarchical structure (bi/tri-level decision-making). Kao et al [18] established a multi-objective programming method to solve the network data envelopment analysis model; Dujardin et al [19] established a multi-objective interactive system for adaptive traffic control; Zhou et al [20] proposed a selection hyper-heuristic based algorithm for solving multi-objective optimization problems; for the solving algorithm for Multi-objective binary programs, Boland et al [11] discussed the theory foundation of preprocessing and cut generation techniques guaranteeing the existence of a feasible solution.…”

Multi-view Genetic Programming Learning to Obtain Interpretable Rule-Based Classifiers for Semi-supervised Contexts. Lessons Learnt

“…Therefore, multiple-induced rule-based systems are precisely subject of the multi-view approach, where the joint learning may produce better single systems even from the same dataset. A widely applied search paradigm to induce rule-based classifiers is genetic programming [22][23][24]. It applies the biological evolutionary metaphor on individuals that represent classification rules to generate robust classifiers.…”