This paper presents a classification approach based on attribute learning for high spatial resolution Synthetic Aperture Radar (SAR) images. To explore the representative and discriminative attributes of SAR images, first, an iterative unsupervised algorithm is designed to cluster in the low-level feature space, where the maximum edge response and the ratio of mean-to-variance are included; a c...

Attribute selection is generally considered as a challenging work in the development of image data mining oriented applications. Attribute subset selection is mainly an optimization problem, which involves searching the space of possible feature subsets to select the one that is optimal or nearly optimal with respect to the performance measures accuracy, complexity etc., of the application. Thi...

The attribute reduction algorithms based on neighborhood approximation usually use the distance as the approximate metric. Algorithms could result in the loss of information with the same distance threshold to construct the neighborhood families of different dimension spaces. Thereby, an attribute reduction algorithm based on hierarchical granulation is proposed. This algorithm can reduce redun...

Attribute selection is generally considered as a challenging work in the development of image data mining oriented applications. Attribute subset selection is mainly an optimization problem, which involves searching the space of possible feature subsets to select the one that is optimal or nearly optimal with respect to the performance measures accuracy, complexity etc., of the application. Thi...

auctions have been as a competitive method of buying and selling valuable or rare items for a long time. single-sided auctions in which participants negotiate on a single attribute (e.g. price) are very popular. double auctions and negotiation on multiple attributes create more advantages compared to single-sided and single-attribute auctions. nonetheless, this adds the complexity of the auctio...

One major challenge in the field of machine learning, especially in classification problems, is to optimize the attribute space in order to obtain a classification function, which will be used to discriminate future items. Several approaches to optimize the attribute space can be used: some of them select the most relevant attributes and the other ones extract certain attributes to create a new...