background: numerous studies used microarray gene expression data to extract metastasis-driving gene signatures for the prediction of breast cancer relapse. however, the accuracy and generality of the previously introduced biomarkers are not acceptable for reliable usage in independent datasets. this inadequacy is attributed to ignoring gene interactions by simple feature selection methods, due...

biomedical datasets usually include a large number of features relative to the number of samples. however, some data dimensions may be less relevant or even irrelevant to the output class. selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. to this end, this paper presents a hybrid method of filter and wr...

The basic principles of a support vector machine (SVM) are analyzed. The problem of feature selection while using an SVM is speci®cally addressed. An approach to constructing a kernel function which takes into account some domain knowledge about a problem and thus essentially diminishes the number of noisy parameters in high dimensional feature space is suggested. Its application to Texture Rec...

In this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. Then, for optimizing the initial feature vector, a genetic algorithm is proposed. Some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and K-nearest neig...

hyperspectral sensors provide a large number of spectral bands. this massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. we propose to use overlap-based feature weigh...

We present a shape classification technique for structural content–based retrieval of two-dimensional vector drawings. Our method has two distinguishing features. For one, it relies on explicit hierarchical descriptions of drawing structure by means of spatial relationships and shape characterization. However, unlike other approaches which attempt rigid shape classification, our method relies o...