TY - CHAP
T1 - Study on Class Imbalance Problem with Modified KNN for Classification
AU - Sasirekha, R.
AU - Kanisha, B.
AU - Kaliraj, S.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Identification of data imbalance is a very challenging one in the modern era. When we go for a data warehouse, there would be a vast data available in it but managing data and sustaining the balanced state of data is very difficult to handle in any type of sector. Occurrence of data imbalance comes when specimens are classified based on their behaviour. In this paper, the imbalance state of data is analysed and the machine learning techniques are studied carefully to choose the best technique to handle data imbalance problems. Wide analysis of the k-nearest neighbour (KNN) algorithm can be carried out to keep the classification of specimens grouped equally.
AB - Identification of data imbalance is a very challenging one in the modern era. When we go for a data warehouse, there would be a vast data available in it but managing data and sustaining the balanced state of data is very difficult to handle in any type of sector. Occurrence of data imbalance comes when specimens are classified based on their behaviour. In this paper, the imbalance state of data is analysed and the machine learning techniques are studied carefully to choose the best technique to handle data imbalance problems. Wide analysis of the k-nearest neighbour (KNN) algorithm can be carried out to keep the classification of specimens grouped equally.
UR - http://www.scopus.com/inward/record.url?scp=85126353901&partnerID=8YFLogxK
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U2 - 10.1007/978-981-16-7610-9_15
DO - 10.1007/978-981-16-7610-9_15
M3 - Chapter
AN - SCOPUS:85126353901
T3 - Lecture Notes on Data Engineering and Communications Technologies
SP - 207
EP - 217
BT - Lecture Notes on Data Engineering and Communications Technologies
PB - Springer Science and Business Media Deutschland GmbH
ER -