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Semantic-k-NN algorithm: An enhanced version of traditional k-NN algorithm

Ali, M. and Jung, L.T. and Abdel-Aty, A.-H. and Abubakar, M.Y. and Elhoseny, M. and Ali, I. (2020) Semantic-k-NN algorithm: An enhanced version of traditional k-NN algorithm. Expert Systems with Applications, 151 .

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

The k-NN algorithm is one of the most renowned ML algorithms widely used in the area of data classification research. With the emergence of big data, the performance and the efficiency of the traditional k-NN algorithm is fast becoming a critical issue. The traditional k-NN algorithm is inefficient to solve the high volume multi-categorical training datasets Traditional k-NN algorithm has a constraint in filtering the training dataset to yield training data that are most relevant to the intended or the targeted test dataset/file. It has to scan through all the training datasets categories to classify the intended/targeted data. As such, traditional k-NN is considered not intelligent and consequently is suffering poor accuracy performance with high computational complexity. A Semantic-kNN (Sk-NN) algorithm for ML is thus proposed in this paper to address the limitations in the traditional k-NN. The proposed Sk-NN deploys a process by leveraging on the semantic itemization and bigram model to filter the training dataset in accordance with the relevant information engaged in the test dataset. It is aimed for general security applications such as finding (the confidentiality level of the data when the algorithm is trained with multiple training categories during the data classification phase. Ultimately, Sk-NN is to elevate the ML performance in pattern extraction and labeling in the big data context. © 2020

Item Type:Article
Impact Factor:cited By 16
Uncontrolled Keywords:Big data; Data Analytics; Learning systems; Nearest neighbor search; Pattern recognition; Semantics; Statistical tests, Critical issues; Data classification; k-NN algorithm; Pattern extraction; Security application; Training data; Training data sets; Training dataset, Classification (of information)
ID Code:23434
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 07:20
Last Modified:19 Aug 2021 07:20

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