A Brief Review of Nearest Neighbor Algorithm for Learning and Classification
Kashvi TaunkSchool of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, IndiaSanjukta DeSchool of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, IndiaSrishti VermaSchool of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, IndiaAleena SwetapadmaSchool of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India
2019en
ABI
Abstract
k-Nearest Neighbor (kNN) algorithm is an effortless but productive machine learning algorithm. It is effective for classification as well as regression. However, it is more widely used for classification prediction. kNN groups the data into coherent clusters or subsets and classifies the newly inputted data based on its similarity with previously trained data. The input is assigned to the class with which it shares the most nearest neighbors. Though kNN is effective, it has many weaknesses. This paper highlights the kNN method and its modified versions available in previously done researches. These variants remove the weaknesses of kNN and provide a more efficient method.
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