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Advances in feature selection for da...

Jain, Lakhmi C.

Advances in feature selection for data and pattern recognition

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advances in feature selection for data and pattern recognitionedited by Urszula Stanczyk, Beata Zielosko, Lakhmi C. Jain.
其他作者:	Stanczyk, Urszula.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	xviii, 328 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Pattern recognition systems.
電子資源:	http://dx.doi.org/10.1007/978-3-319-67588-6
ISBN:	9783319675886$q(electronic bk.)

Advances in feature selection for data and pattern recognition
Advances in feature selection for data and pattern recognition[electronic resource] /edited by Urszula Stanczyk, Beata Zielosko, Lakhmi C. Jain. - Cham :Springer International Publishing :2018. - xviii, 328 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.1381868-4394 ;. - Intelligent systems reference library ;v.24..

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.

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, professors and practitioners.

ISBN: 9783319675886$q(electronic bk.)

Standard No.: 10.1007/978-3-319-67588-6doiSubjects--Topical Terms:

183725
Pattern recognition systems.

LC Class. No.: TK7882.P3

Dewey Class. No.: 006.4

Advances in feature selection for data and pattern recognition
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505 0 $a 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.
520 $a 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, professors and practitioners.
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