A Multiobjective Genetic Algorithm for Attribute Selection
نویسندگان
چکیده
The problem of feature selection in data mining is an important real-world problem that involves multiple objectives to be simultaneously optimized. In order to tackle this problem this work proposes a multiobjective genetic algorithm for feature selection based on the wrapper approach. The algorithm’s main goal is to find the best subset of features that minimizes both the error rate and the size of the tree discovered by a classification algorithm, namely C4.5, using the Pareto dominance concept.
منابع مشابه
Attribute Selection with a Multi-objective Genetic Algorithm
In this paper we address the problem of multiobjective attribute selection in data mining. We propose a multiobjective genetic algorithm (GA) based on the wrapper approach to discover the best subset of attributes for a given classification algorithm, namely C4.5, a well-known decision-tree algorithm. The two objectives to be minimized are the error rate and the size of the tree produced by C4....
متن کاملApplication of genetic algorithm (GA) to select input variables in support vector machine (SVM) for analyzing the occurrence of roach, Rutilus rutilus, in streams
Support vector machine (SVM) was used to analyze the occurrence of roach in Flemish stream basins (Belgium). Several habitat and physico?chemical variables were used as inputs for the model development. The biotic variable merely consisted of abundance data which was used for predicting presence/absence of roach. Genetic algorithm (GA) was combined with SVM in order to select the most important...
متن کاملMultiobjective Imperialist Competitive Evolutionary Algorithm for Solving Nonlinear Constrained Programming Problems
Nonlinear constrained programing problem (NCPP) has been arisen in diverse range of sciences such as portfolio, economic management etc.. In this paper, a multiobjective imperialist competitive evolutionary algorithm for solving NCPP is proposed. Firstly, we transform the NCPP into a biobjective optimization problem. Secondly, in order to improve the diversity of evolution country swarm, and he...
متن کاملMultiobjective genetic fuzzy rule selection of single granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions
Multiobjective genetic fuzzy rule selection is based on the generation of a set of candidate fuzzy classification rules using a preestablished granularity or multiple fuzzy partitions with different granularities for each attribute. Then, a multiobjective evolutionary algorithm is applied to perform fuzzy rule selection. Since using multiple granularities for the same attribute has been sometim...
متن کاملMonitoring process variability: a hybrid Taguchi loss and multiobjective genetic algorithm approach
The common consideration on economic model is that there is knowledge about the risk of occurrence of an assignable cause and the various cost parameters that does not always adequately describe what happens in practice. Hence, there is a need for more realistic assumptions to be incorporated. In order to reduce cost penalties for not knowing the true values of some parameters, this paper aims ...
متن کامل