Hybrid Genetic Algorithm for Classifier Ensemble Selection

Fault Detection of Bearings Using a Rule-based Classifier Ensemble and Genetic Algorithm

This paper proposes a reduct construction method based on discernibility matrix simplification. The method works with genetic algorithm. To identify potential problems and prevent complete failure of bearings, a new method based on rule-based classifier ensemble is presented. Genetic algorithm is used for feature reduction. The generated rules of the reducts are used to build the candidate base...

MLIFT: Enhancing Multi-label Classifier with Ensemble Feature Selection

Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label classifier which utilizes a new strategy to multi-label learning by leveraging label-specific ...

A Genetic Algorithm to Ensemble Feature Selection

Intelligent and Robust Genetic Algorithm Based Classifier

The concepts of robust classification and intelligently controlling the search process of genetic algorithm (GA) are introduced and integrated with a conventional genetic classifier for development of a new version of it, which is called Intelligent and Robust GA-classifier (IRGA-classifier). It can efficiently approximate the decision hyperplanes in the feature space. It is shown experime...

A Hybrid Genetic Algorithm using Probabilistic Selection

A number of algorithms and strategies and their variations are currently being used for solving complex optimization problems. Genetic algorithms (GAs) are one of the best strategies for solving such problems basically due to their inherent parallel search capability. Other methods found useful in diverse application areas are simulated annealing, evolution strategies etc. The searching ability...