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: 12544 HUF
Availability: InStock

Szerző: Marwa Al-Roby, Alaa ElHalees

Nyelv:

Kötés: Puha kötésű

Kiadó: LAP Lambert Academic Publishing

Elérhetőség: Beszállítói készleten

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

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Szerző

Marwa Al-Roby, Alaa ElHalees

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2017

oldal

EAN

9783330018464

Enbook ID

15875345

Kiadó

LAP Lambert Academic Publishing

Súly

137

Méretek

150 x 220 x 5

Teljes leírás

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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