Unstructured Data Classification: Uncertain Nearest Neighbor Decision Rule (Paperback or Softback)

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

ISBN 10: 1636480497 / ISBN 13: 9781636480497

Published by 12/3/2020, 2020

About this Item

Unstructured Data Classification: Uncertain Nearest Neighbor Decision Rule (Paperback or Softback).

Seller Inventory # BBS-9781636480497

Bibliographic Details

• Title: Unstructured Data Classification: Uncertain Nearest Neighbor Decision Rule (Paperback or Softback)
• Publisher: 12/3/2020
• Publication Date: 2020
• Language: English
• ISBN 10: 1636480497
• ISBN 13: 9781636480497
• Binding: Soft cover
• Condition: New
• Weight: 0.29 pounds

About this title

Synopsis

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.