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New Method to Improve Mining of Multi-Class Imbalanced Data - Couverture souple

Al-Roby, Marwa; ElHalees, Alaa

 
9783330018464: New Method to Improve Mining of Multi-Class Imbalanced Data
Couverture souple
ISBN 10 : 3330018461 ISBN 13 : 9783330018464
Editeur : LAP LAMBERT Academic Publishing, 2017

Synopsis

Class imbalance is one of the challenging problems for data mining and machine learning techniques. The data in real-world applications often has imbalanced class distribution. That is occur when most examples are belong to a majority class and few example belong to a minority class. In this case, standard classifiers tend to classify all examples as a majority class and completely ignore the minority class. For this problem, researchers proposed a lot of solutions at both data and algorithmic levels. Most efforts concentrate on binary class problems. However, binary class is not the only scenario where the class imbalance problem prevails. In the case of multi-class data sets, it is much more difficult to define the majority and minority classes. Hence, multi class classification in imbalanced data sets remains an important topic of research. In our Book, we proposed new approach based on SOMTE (Synthetic Minority Over-sampling TEchnique) and clustering which is able to deal with imbalanced data problem involving multiple classes. We implemented our approach by using open source machine learning tools: Weka, and RapidMiner.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Class imbalance is one of the challenging problems for data mining and machine learning techniques. The data in real-world applications often has imbalanced class distribution. That is occur when most examples are belong to a majority class and few example belong to a minority class. In this case, standard classifiers tend to classify all examples as a majority class and completely ignore the minority class. For this problem, researchers proposed a lot of solutions at both data and algorithmic levels. Most efforts concentrate on binary class problems. However, binary class is not the only scenario where the class imbalance problem prevails. In the case of multi-class data sets, it is much more difficult to define the majority and minority classes. Hence, multi class classification in imbalanced data sets remains an important topic of research. In our Book, we proposed new approach based on SOMTE (Synthetic Minority Over-sampling TEchnique) and clustering which is able to deal with imbalanced data problem involving multiple classes. We implemented our approach by using open source machine learning tools: Weka, and RapidMiner.

Biographie de l'auteur

Marwa F. Al-Roby, IT Lecturer, Holding a master degree in Information technology. Currently working as IT Lecturer in Saudi Arabia. Alaa M. ElHalees, Professor of Computer Science, Holding a PhD in Data mining, Currently working as Proff. at Faculty of Information Technology at Islamic University of Gaza.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2017
Langue
anglais
ISBN 10 
3330018461
ISBN 13 
9783330018464
Reliure
Broché
Nombre de pages
80
Coordonnées du fabricant
non disponible
Personne responsable
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