Feature selection (also known as subset selection) is a process commonly used in machine learning, wherein a subset of the features available from the data are selected for application of a learning algorithm. The best subset contains the least number of dimensions that most contribute to accuracy; we discard the remaining, unimportant dimensions. This is an important stage of preprocessing and...

The management of coupon promotions is an important issue for marketing managers since it still is the major promotion medium. However, the distribution of coupons does not go without problems. Although manufacturers and retailers are investing heavily in the attempt to convince as many customers as possible, overall coupon redemption rate is low. This study improves the strategy of retailers a...

3D scene analysis by automatically assigning 3D points a semantic label has become an issue of major interest in recent years. Whereas the tasks of feature extraction and classification have been in the focus of research, the idea of using only relevant and more distinctive features extracted from optimal 3D neighborhoods has only rarely been addressed in 3D lidar data processing. In this paper...

This paper summarizes our work and our understanding on volumetric pathological neuroimage retrieval under the framework of classification-driven feature selection. The main effort concerns off-line image feature space reduction for improved image indexing feature discriminating power as well as reduced computational cost during on-line pathological neuroimage retrieval. Keywrods: 3D image, fea...

In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on quality is therefore a crucial preprocessing step. We propose novel selection algorithm based quadratic unconstrained binary optimization (QUBO) problem, which allows to select specified number their importance and redundancy. contrast iterative or greedy methods, our direct a...

This paper deals with the problem of integrating most suitable feature selection methods for a given in order to achieve best order. A new, adaptive and hybrid approach is proposed, which combines utilizes multiple individual more generalized solution. Various state-of-the-art are presented detail examples their applications an exhaustive evaluation conducted measure compare performance propose...