Similarity Measure Development for Case-Based Reasoning- A Data-driven Approach
This work addresses the need for improved similarity modeling in case-based reasoning systems, but it appears incremental as it builds on existing methods with a data-driven focus.
The paper tackles the problem of developing similarity measures for case-based reasoning by proposing a data-driven approach that models local similarity measures for numerical and categorical attributes, resulting in a CBR system that can search for the most relevant similar cases.
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.