Unstructured Data Classification: Uncertain Nearest Neighbor Decision Rule - Brossura

Thippeswamy, Dr. K.; G. S., Dr. Nijaguna

Brossura

ISBN 10: 1636480497 ISBN 13: 9781636480497

Casa editrice: Eliva Press, 2020

Riassunto

According to certain criteria, the classes are identified by using classification techniques, which is considered as data mining tool. When compared with smaller class, the classification results (i.e., accuracy) for bigger class are deviating and the traditional classification procedures provides inaccurate results, which is known as Class Imbalance problem. A class is formed with unequal size, where this type of data is represented and combined as class imbalance data. There are two various categories are presents in class imbalance domain, namely minority (i.e., smaller) and majority (i.e., bigger) classes. The major aim of this research work is to identify the minority class accurately. In this research, two significant methodologies are proposed such as (i) Adaptive-Condensed Nearest Neighbor (ACNN)Algorithm, and (ii) Local Mahalanobis Distance Learning(LMDL) based ACNN algorithm. These methods are significantly improving the imbalanced data classification.

Dettagli dell'edizione

• Editore: Eliva Press
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 1636480497
• ISBN 13: 9781636480497
• Rilegatura: Copertina flessibile
• Numero di pagine: 89
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile