Multi-Scale Fuzzy Feature Selection Method applied to Wood Singularity Identification

Multi-Scale Feature Selection in Stereo

In binocular stereo matching, points in left and right images are matched according to features that characterize each point and identify pairs of points. When one tries to use multiple features, a difficult problem is which feature, or combination of features, to use. Moreover, features are difficult to crossnormalize and so comparisons must take into account not only their output, but also th...

Multi-scale feature identification using evolution strategies

An image usually contains a number of different features and regions. Many image-related applications, such as content-based image retrieval and MRI-based diagnosis, often require the ability to identify and mark features within the image. For images containing a specific sort of feature (e.g. convective storm) or region (e.g. earthquake debris), that feature or region is always located adjacen...

Multi-Scale Singularity Trees (MSSTs)

Ant Feature Selection Applied to Marble Classification

One of the most important techniques in data preprocessing for data mining is feature selection. Real-world data analysis, data mining, classification and modeling problems usually involve a large number of candidate inputs or features. This paper proposes an ant colony optimization (ACO) algorithm for the feature selection problem. The goal is to find the set of features that reveals the best ...

A hybrid filter-based feature selection method via hesitant fuzzy and rough sets concepts

High dimensional microarray datasets are difficult to classify since they have many features with small number ofinstances and imbalanced distribution of classes. This paper proposes a filter-based feature selection method to improvethe classification performance of microarray datasets by selecting the significant features. Combining the concepts ofrough sets, weighted rough set, fuzzy rough se...