A new text-based w-distance metric to find the perfect match between words

• Main Authors: Ali, M., Jung, L.T., Hosam, O., Wagan, A.A., Shah, R.A., Khayyat, M.
• Format: Article
• Published: IOS Press 2020
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081537860&doi=10.3233%2fJIFS-179552&partnerID=40&md5=2f8ba723a873404c83134a9f0365fe1b; http://eprints.utp.edu.my/23450/

The k-NN algorithm is an instance-based learning algorithm which is widely used in the data mining applications. The core engine of the k-NN algorithm is the distance/similarity function. The performance of the k-NN algorithm varies with the selection of distance function. The traditional distance/similarity functions in k-NN do not perfectly handle the mix-mode words such as when one string has multiple substrings/words. For example, a two-word string of 'Employee Name', a one-word string of 'Name' or more than one word such as, 'Name of Employee'. This ambiguity is faced by different distance/similarity functions causing difficulties in finding the perfect match of words. To improve the perfect-match calculation functionality in the traditional k-NN algorithm, a new similarity distance metric is developed and named as word-distance (w-distance). The perfect match will help us to identify the exact required value. The proposed w-distance is a hybrid of distance and similarity in nature because it is to handle dissimilarity and similarity features of strings at the same time. The simulation results showed that w-distance has a better impact on the performance of the k-NN algorithm as compared to the Euclidean distance and the cosine similarity. © 2020-IOS Press and the authors. All rights reserved.