New Method to Improve Mining of Multi-Class Imbalanced Data

Name: New Method to Improve Mining of Multi-Class Imbalanced Data
Brand: LAP Lambert Academic Publishing
SKU: 9783330018464
Price: 32.96 EUR
Availability: InStock

Автор: Marwa Al-Roby, Alaa ElHalees

Език:

Корици: С меки корици

Издател: LAP Lambert Academic Publishing

Наличност: Външен склад

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Class imbalance is one of the challenging problems for data mining and machine learning techniques....

Информация за книгата

Автор

Marwa Al-Roby, Alaa ElHalees

Език

Английски език

Корици

Книга - С меки корици

Издадена

2017

страници

EAN

9783330018464

Enbook ID

15875345

Издател

LAP Lambert Academic Publishing

Теглоt

137

Размери

150 x 220 x 5

Пълно описание

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.

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New Method to Improve Mining of Multi-Class Imbalanced Data

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