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Sinopsis
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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Reseña del editor
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.
Biografía del autor
Dharmendra Singh Rajput - Ph.D. (January 2015) in the area of Document Clustering from National Institute of Technology Bhopal, India. Has 6+ Years’ Experience in Teaching, Research and industry. Currently, works as an Associate Professor in School of Information Technology & Engineering, VIT University, Vellore India.
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- EditorialLAP LAMBERT Academic Publishing
- Año de publicación2017
- ISBN 10 3659914215
- ISBN 13 9783659914218
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas76
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Analysis of classification for imbalance data
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LAP LAMBERT Academic Publishing Mrz 2017, 2017
ISBN 10: 3659914215
ISBN 13: 9783659914218
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 76 pp. Englisch. Nº de ref. del artículo: 9783659914218
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Analysis of classification for imbalance data
Publicado por
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3659914215
ISBN 13: 9783659914218
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783659914218
Cantidad disponible: 1 disponibles
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Analysis of classification for imbalance data
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LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3659914215
ISBN 13: 9783659914218
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Analysis of classification for imbalance data
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LAP LAMBERT Academic Publishing Mär 2017, 2017
ISBN 10: 3659914215
ISBN 13: 9783659914218
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 76 pp. Englisch. Nº de ref. del artículo: 9783659914218
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Analysis of classification for imbalance data
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LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3659914215
ISBN 13: 9783659914218
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Paperback. Condición: Brand New. 76 pages. 8.66x5.91x0.18 inches. In Stock. Nº de ref. del artículo: 3659914215
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