Advances in Feature Selection for Data and Pattern Recognition
Author:   Urszula Stańczyk ,  Beata Zielosko ,  Lakhmi C. Jain
Publisher:   Springer International Publishing AG
Edition:   1st ed. 2018
Volume:   138
ISBN:  

9783319675879


Pages:   328
Publication Date:   27 November 2017
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Advances in Feature Selection for Data and Pattern Recognition


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Overview

This book presents recent developments and research trends in the field of feature selection for data and pattern recognition, highlighting a number of latest advances. The field of feature selection is evolving constantly, providing numerous new algorithms, new solutions, and new applications. Some of the advances presented focus on theoretical approaches, introducing novel propositions highlighting and discussing properties of objects, and analysing the intricacies of processes and bounds on computational complexity, while others are dedicated to the specific requirements of application domains or the particularities of tasks waiting to be solved or improved. Divided into four parts – nature and representation of data; ranking and exploration of features; image, shape, motion, and audio detection and recognition; decision support systems, it is of great interest to a large section of researchers including students, professorsand practitioners.

Full Product Details

Author:   Urszula Stańczyk ,  Beata Zielosko ,  Lakhmi C. Jain
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2018
Volume:   138
Weight:   6.447kg
ISBN:  

9783319675879


ISBN 10:   3319675877
Pages:   328
Publication Date:   27 November 2017
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

An Introduction.- Attribute Selection Based on Reduction of Numerical Attribute During Discretization.- Improving Bagging Ensembles for Class Imbalanced Data by Active Learning.- Optimization of Decision Rules Relative to Length Based on Modified Dynamic Programming Approach.- Ranking-Based Rule Classifier Optimisation.- Attribute Selection in a Dispersed Decision-Making System.- Feature Selection Approach for Rule-based Knowledge Bases.- Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data.

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