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Unstructured Data Classification: Uncertain Nearest Neighbor Decision Rule (en Inglés)
Nijaguna G. S.
(Autor)
·
K. Thippeswamy
(Autor)
·
Eliva Press
· Tapa Blanda
Unstructured Data Classification: Uncertain Nearest Neighbor Decision Rule (en Inglés) - G. S., Nijaguna ; Thippeswamy, K.
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Reseña del libro "Unstructured Data Classification: Uncertain Nearest Neighbor Decision Rule (en Inglés)"
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.
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