Study on Class Imbalance Problem with Modified KNN for Classification

R. Sasirekha; B. Kanisha; S. Kaliraj

doi:10.1007/978-981-16-7610-9_15

Study on Class Imbalance Problem with Modified KNN for Classification

R. Sasirekha^*
, B. Kanisha
, S. Kaliraj

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter

6 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Lecture Notes on Data Engineering and Communications Technologies
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	207-217
Number of pages	11
DOIs	https://doi.org/10.1007/978-981-16-7610-9_15
Publication status	Published - 2022

Publication series

Name	Lecture Notes on Data Engineering and Communications Technologies
Volume	101
ISSN (Print)	2367-4512
ISSN (Electronic)	2367-4520

All Science Journal Classification (ASJC) codes

Information Systems
Media Technology
Computer Science Applications
Computer Networks and Communications
Electrical and Electronic Engineering

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10.1007/978-981-16-7610-9_15

Cite this

Sasirekha, R., Kanisha, B., & Kaliraj, S. (2022). Study on Class Imbalance Problem with Modified KNN for Classification. In Lecture Notes on Data Engineering and Communications Technologies (pp. 207-217). (Lecture Notes on Data Engineering and Communications Technologies; Vol. 101). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-7610-9_15

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abstract = "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.",

author = "R. Sasirekha and B. Kanisha and S. Kaliraj",

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Sasirekha, R, Kanisha, B & Kaliraj, S 2022, Study on Class Imbalance Problem with Modified KNN for Classification. in Lecture Notes on Data Engineering and Communications Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol. 101, Springer Science and Business Media Deutschland GmbH, pp. 207-217. https://doi.org/10.1007/978-981-16-7610-9_15

Study on Class Imbalance Problem with Modified KNN for Classification. / Sasirekha, R.; Kanisha, B.; Kaliraj, S.
Lecture Notes on Data Engineering and Communications Technologies. Springer Science and Business Media Deutschland GmbH, 2022. p. 207-217 (Lecture Notes on Data Engineering and Communications Technologies; Vol. 101).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Study on Class Imbalance Problem with Modified KNN for Classification

AU - Sasirekha, R.

AU - Kanisha, B.

AU - Kaliraj, S.

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.

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EP - 217

BT - Lecture Notes on Data Engineering and Communications Technologies

PB - Springer Science and Business Media Deutschland GmbH

ER -

Study on Class Imbalance Problem with Modified KNN for Classification

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