Novel associative classifier based on dynamic adaptive PSO: Application to determining candidates for thoracic surgery

• Published in: Expert systems with applications Vol. 41; no. 18; pp. 8234 - 8244
• Main Authors: Mangat, Veenu, Vig, Renu
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 15.12.2014; Elsevier
• Subjects: Algorithms; Applied sciences; Associative; Associative classification; Biological and medical sciences; Classification; Classifiers; Computer science; control theory; systems; Data mining; Data processing. List processing. Character string processing; Dynamics; Environment. Living conditions; Exact sciences and technology; Medical sciences; Memory organisation. Data processing; PSO; Public health. Hygiene; Public health. Hygiene-occupational medicine; Rule quality; Searching; Software; Surgery; Associative classification; Rule quality; PSO; Evolutionary algorithm; Updating; Data mining; Seeding; Adaptive method; Teaching; Education; Surgery; Classification; Knowledge discovery; Agriculture; Swarm intelligence; Medical application; Search problem; Reconstruction; Data analysis; Life expectancy; Statistical association; Thorax; Experimental study; Particle swarm optimization; Search algorithm; Association scheme; Medicine; Lifetime; Seed; Heuristic method
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2014.06.046

•Novel associative classifier based on modified Particle Swarm Optimization (PSO).•Uses local, global and personal learning; dynamic regions and adaptive parameters.•Quality evaluation is done for individual rules as well as rule sets.•Results show superior performance than fourteen state-of-the-art classifiers.•Method is successfully applied to a practical medical domain problem. Association rule mining is a data mining technique for discovering useful and novel patterns or relationships from databases. These rules are simple to infer and intuitive and can be easily used for classification in any domain that requires explanation for and investigation into how the classification works. Examples of such areas are medicine, agriculture, education, etc. For such a system to find wide adoptability, it should give output that is correct and comprehensible. The amount of data has been growing very fast and so has the search space of these problems. So we need to change traditional methods. This paper discusses a rule mining classifier called DA-AC (dynamic adaptive-associative classifier) which is based on a Dynamic Particle Swarm Optimizer. Due to its seeding method, exemplar selection, adaptive parameters, dynamic reconstruction of regions and velocity update, it avoids premature convergence and provides a better value in every dimension. Quality evaluation is done both for individual rules as well as entire rulesets. Experiments were conducted over fifteen benchmark datasets to evaluate performance of proposed algorithm in comparison with six other state-of-the-art non associative classifiers and eight associative classifiers. Results demonstrate competitive performance of proposed DA-AC while considering predictive accuracy and number of mined patterns as parameters. The method was then applied to predict life expectancy of post operative thoracic surgery patients.