Clustering based Feature Selection from High Dimensional Data

Data mining techniques have been widely applied to extract knowledge from large databases. Data mining searches for relationships and global patterns that exist in large databases that are ‘hidden’ among the huge data. Feature selection involves selecting the most useful features from the given data set and reduces dimensionality. Graph clustering method is used for feature selection. Features which are most relevant to the target class and independent of other are selected from the cluster. The feature subset obtained are given to the various supervised learning algorithms to increase the learning accuracy and obtain best feature subset. The feature selection can be efficient and effective using clustering approach. Based on the criteria of efficiency in terms of time complexity and effectiveness in terms of quality of data, useful features from the big data can be selected. DOI: 10.17762/ijritcc2321-8169.15061

Search Strategies for Ensemble Feature Selection in Medical Diagnostics

The goal of this paper is to propose, evaluate, and compare four search strategies for ensemble feature selection, and to consider their application to medical diagnostics, with a focus on the problem of the classification of acute abdominal pain. Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to get higher accuracy, sensitivity, and specificity, which are often not achievable with single models. One technique, which proved to be effective for ensemble construction, is feature selection. Lately, several strategies for ensemble feature selection were proposed, including random subspacing, hill-climbing-based search, and genetic search. In this paper, we propose two new sequential-search-based strategies for ensemble feature selection, and evaluate them, constructing ensembles of simple Bayesian classifiers for the problem of acute abdominal pain classification. We compare the search strategies with regard to achieved accuracy, sensitivity, specificity, and the average number of features they select

Search Strategies for Ensemble Feature Selection in Medical Diagnostics

TCD-CS-2003-22The goal of this paper is to propose, evaluate, and compare four search strategies for ensemble feature selection, and to consider their application to medical diagnostics, with a focus on the problem of the classification of acute abdominal pain. Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to get higher accuracy, sensitivity, and specificity, which are often not achievable with single models. One technique, which proved to be effective for ensemble construction, is feature selection. Lately, several strategies for ensemble feature selection were proposed, including random subspacing, hill-climbing-based search, and genetic search. In this paper, we propose two new sequential-search-based strategies for ensemble feature selection, and evaluate them, constructing ensembles of simple Bayesian classifiers for the problem of acute abdominal pain classification. We compare the search strategies with regard to achieved accuracy, sensitivity, specificity, and the average number of features they select