Hyperspectral images (HSIs) showing objects belonging to several distinct target classes are characterized by dozens of spectral bands being available. However, some these redundant and/or noisy, and hence, selecting highly informative trustworthy for each class is a vital step classification saving internal storage space; then the selected termed band subset. We use mutual information (MI)-bas...

objective: diabetes is one of the most common metabolic diseases. earlier diagnosis of diabetes and treatment of hyperglycemia and related metabolic abnormalities is of vital importance. diagnosis of diabetes via proper interpretation of the diabetes data is an important classification problem. classification systems help the clinicians to predict the risk factors that cause the diabetes or pre...

We show the use of a genetic algorithm for feature subset selection over feature vectors that describe the system calls executed by privileged processes. Genetic feature subset selection significantly reduces the number of features used without adversely affecting the accuracy of the predictions.

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...

The Clustering is a method of grouping the information into modules or clusters. Their dimensionality increases usually with a tiny number of dimensions that are significant to definite clusters, but data in the unrelated dimensions may produce much noise and wrap the actual clusters to be exposed. Attribute subset selection method is frequently used for data reduction through removing unrelate...

A Feature selection algorithm employ for removing irrelevant, redundant information from the data. Amongst feature subset selection algorithm filter methods are used because of its generality and are usually good choice when numbers of features are large. In cluster analysis, graph-theoretic clustering methods to features are used. In particular, the minimum spanning tree (MST)based clustering ...

this study investigates the detection of the drowsiness state for a future application such as in the reduction ofthe road traffic accidents. the electroencephalography(eeg), electrooculography (eog), driving quality (dq), and karolinska sleepiness scale (kss) data of 7 male during approximately 20 hours of sleep deprivation were recorded. to reduce the eye blink artifact, an automatic mechanis...

In this paper, we identify two issues involved in developing an automated feature subset selection algorithm for unlabeled data: the need for finding the number of clusters in conjunction with feature selection, and the need for normalizing the bias of feature selection criteria with respect to dimension. We explore the feature selection problem and these issues through FSSEM (Feature Subset Se...

Many feature subset selection algorithms have been proposed, but not all of them are appropriate for a given feature selection problem. At the same time, so far there is rarely a good way to choose appropriate feature subset selection algorithms for the problem at hand. Feature selection has become an essential element in the Data Mining process. In this paper, investigate the problem of effici...