Similarity Measure Development for Case-Based Reasoning- A Data-driven Approach
Artificial Intelligence
2019-05-22 v1
Abstract
In this paper, we demonstrate a data-driven methodology for modelling the local similarity measures of various attributes in a dataset. We analyse the spread in the numerical attributes and estimate their distribution using polynomial function to showcase an approach for deriving strong initial value ranges of numerical attributes and use a non-overlapping distribution for categorical attributes such that the entire similarity range [0,1] is utilized. We use an open source dataset for demonstrating modelling and development of the similarity measures and will present a case-based reasoning (CBR) system that can be used to search for the most relevant similar cases.
Cite
@article{arxiv.1905.08581,
title = {Similarity Measure Development for Case-Based Reasoning- A Data-driven Approach},
author = {Deepika Verma and Kerstin Bach and Paul Jarle Mork},
journal= {arXiv preprint arXiv:1905.08581},
year = {2019}
}