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Analysis of classification for imbalance data

Name: Analysis of classification for imbalance data
Brand: LAP Lambert Academic Publishing
SKU: 9783659914218
Price: 135.33 PLN
Availability: InStock

Autor: Dharmendra Singh Rajput, S. Sinduja

Język:

Oprawa: Miękka

Wydawca: LAP Lambert Academic Publishing

Dostępność: Dostępna u dostawcy

Wysyłamy za 5-8 dni

135.33 zł

An enormous amount of data is being collected and stored in databases everywhere across the world. T...

Informacje o książce

Autor

Dharmendra Singh Rajput, S. Sinduja

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2017

strony

EAN

9783659914218

Enbook ID

16191185

Wydawca

LAP Lambert Academic Publishing

Waga

131

Wymiary

150 x 220 x 5

Pełny opis

An enormous amount of data is being collected and stored in databases everywhere across the world. These data bundles up and keep on increasing every year. Extracting information that are hidden in such databases and classifying that information extracted are most important tasks in data mining. If such datasets are imbalanced, then it becomes tough to handle it. Since Predicting future is one of the fundamental tasks in data mining. Working with imbalance datasets to predict the possible outcome is a very tedious task. The dataset is imbalanced when it is not classified correctly, when one class holds more instances than other. They are often represented as a positive class (minority) and negative (majority) class. The class that has less number of samples is called minority class, and one that has more is called majority class. Imbalance dataset causes many serious issues in data mining, mostly the standard classification algorithm considers the dataset as balanced which in turn is partial towards majority class. For applications like medical diagnosis, this causes a very serious effect. Hence balancing dataset is critical for many real-time applications.

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