This work tackles the problem of selecting a subset of features in an inductive learning setting, by introducing a novel Thermodynamic Feature Selection algorithm (TFS). Given a suitable objective function, the algorithm makes uses of a specially designed form of simulated annealing to find a subset of attributes that maximizes the objective function. The new algorithm is evaluated against one ...

Feature subset selection is a process of selecting a subset of minimal, relevant features and is a pre processing technique for a wide variety of applications. High dimensional data clustering is a challenging task in data mining. Reduced set of features helps to make the patterns easier to understand. Reduced set of features are more significant if they are application specif ic. Almost all ex...

Genetic Algorithm is used in this paper, which presents the designing an optimized tool for face detection application with idea of “Survival of the fittest”. If higher level of adaption can be achieved, existing systems can perform their functions longer & better. Popular method in feature extraction is principal component analysis (PCA), for feature subset selection used Genetic algorithm (GA...

In this paper, genetic algorithm (GA) is utilized to search for the appropriate feature combination for constructing a maximum entropy (ME) based classifier for named entity recognition (NER). Features are encoded in the chromosomes. The ME classifier is evaluated for the 3-fold cross validation with the features, encoded in a particular chromosome, and its average F-measure value is used as th...

Feature selection is an important task for data analysis and information retrieval processing, pattern classification systems, and data mining applications. It reduces the number of features by removing noisy, irrelevant and redundant data. In this paper, a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. Features are tr...

Feature Selection is to selecting the useful features from the original dataset for improve the more accurate results. Constrained Based Feature Subset Selection(CFSS) Algorithm Removes irrelevant and redundant features. This method is to find a similarity computation based on the entropy and conditional entropy values. After computing similarity computation to applied Approximate Relevancy(AR)...

Basic question arises when classification came in picture classification accuracy, ensemble size, and computational complexity. Feature selection is importance for improvement and performance of classification algorithm. Classification algorithm may not scale up to the size of the full feature set either in sample or time but with feature selection help us to better understand the domain with C...