eprintid: 6771
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/67/71
datestamp: 2023-11-09 16:18:34
lastmod: 2023-11-09 16:18:34
status_changed: 2023-11-09 16:07:37
type: conference_item
metadata_visibility: show
creators_name: Zardari, M.A.
creators_name: Jung, L.T.
title: Data classification with k-NN using novel character frequency-direct word frequency (CF-DWF) similarity formula
ispublished: pub
keywords: Nearest neighbor search; Text processing, complexity; distance/similarity; string; Text classification; TF/IDF, Classification (of information)
note: cited By 4; Conference of 2015 International Symposium on Mathematical Sciences and Computing Research, iSMSC 2015 ; Conference Date: 19 May 2015 Through 20 May 2015; Conference Code:124374
abstract: The k-NN is one of the most popular and easy in implementation algorithm to classify the data. The best thing about k-NN is that it accepts changes with improved version. Despite many advantages of the k-NN, it is also facing many issues. These issues are: distance/similarity calculation complexity, training dataset complexity at classification phase, proper selection of k, and get duplicate values when training dataset is of single class. This paper focuses on only issue of distance/similarity calculation complexity. To avoid this complexity a new distance formula is proposed. The CF-DWF formula is only strings. The CF-DWF is no applicable for other data types. The F1-Score and precision of CF-DWF with k-NN are higher than traditional k-NN. The proposed similarity formula is also efficient than Euclidean Distance (E.D) and Cosine Similarity (C.S). The results section depicts that the k-NN with CF-DWF reduced computational complexity of k-NN with E.D and C.S from 4.77 to 43.69 and improved the F1-Score of traditional k-NN from 12 to 19. © 2015 IEEE.
date: 2016
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995551134&doi=10.1109%2fISMSC.2015.7594066&partnerID=40&md5=449ec4f765f99240969706e2a6057759
id_number: 10.1109/ISMSC.2015.7594066
full_text_status: none
publication: 2015 International Symposium on Mathematical Sciences and Computing Research, iSMSC 2015 - Proceedings
pagerange: 280-285
refereed: TRUE
isbn: 9781479978946
citation:   Zardari, M.A. and Jung, L.T.  (2016) Data classification with k-NN using novel character frequency-direct word frequency (CF-DWF) similarity formula.  In: UNSPECIFIED.