This work presents a hierarchical HMM-based audio segmentation system with feature selection designed for the Albayzin 2010 Evaluations. We propose an architecture that combines the outputs of individual binary detectors which were trained with a specific class-dependent feature set adapted to the characteristics of each class. A fast one-pass-training wrapper-based technique was used to perfor...

introduction: amajor problem in the treatment of cancer is the lack of an appropriate method for the early diagnosis of the disease. the chemical reaction within an organ may be reflected in the form of proteomic patterns in the serum, sputum, or urine. laser mass spectrometry is a valuable tool for extracting the proteomic patterns from biological samples. a major challenge in extracting such ...

In the software quality modeling process, many practitioners often ignore problems such as high dimensionality and class imbalance that exist in data repositories. They directly use the available set of software metrics to build classification models without regard to the condition of the underlying software measurement data, leading to a decline in prediction performance and extension of train...

As a commonly used technique in data preprocessing, feature selection selects a subset of informative attributes or variables to build models describing data. By removing redundant and irrelevant or noise features, feature selection can improve the predictive accuracy and the comprehensibility of the predictors or classifiers. Many feature selection algorithms with different selection criteria ...

This paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique. In this system, we use multi feature extraction such as colour, texture and shape. The three techniques are used for feature extraction such as colour moment, gray level cooccurrence matrix and edge histogram descriptor. To r...

Lasso regression tends to assign zero weights to most irrelevant or redundant features, and hence is a promising technique for feature selection. Its limitation, however, is that it only offers solutions to linear models. Kernel machines with feature scaling techniques have been studied for feature selection with non-linear models. However, such approaches require to solve hard non-convex optim...

Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recogni...

Özellik seçimi, veri analizinde hazırlamak için uygulanan ön işlemlerden biridir. seçimi basitçe orijinal özellik kümesinden en uygun özelliklerin alt kümesinin seçim işlemidir. Bu yöntemler, setinde alakasız ve gereksiz bilgiyi belirlemeye kaldırmaya çalışır. çalışmada sınıf bilgisi kullanılarak değişim katsayısına dayalı yeni bir yöntemi önerilmiştir. Önerilen yönteminin etkinliği, gerçek set...

We present an unsupervised feature dimension reduction method for the classification of magnetic resonance spectra. The technique preserves spectral information, important for disease profiling. We propose to use this technique as a preprocessing step for computationally demanding wrapper-based feature subset selection. We show that the classification accuracy on an independent test set can be ...