Similarity Measure Development for Case-Based Reasoning–A Data-Driven Approach

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.

A review of the development and future challenges of case-based reasoning

“…In addition, there are some other intelligent methods combined with similarity measurement methods to improve the retrieval performance. Verma et al (2019) proposed a data-driven method to model the local similarity measure of numerical and class attributes. Lenz et al (2019) studied an ontology-based semantic similarity measure in the application of argumentation schemes.…”

Using Automated Feature Selection for Building Case-Based Reasoning Systems: An Example from Patient-Reported Outcome Measurements

“…These datasets were used to build CBR systems in a datadriven manner and as training data in the other two regression algorithms. In all the CBR models built for various target outcomes in this work, local similarity modelling of the attributes has been done in the same data-driven manner as presented in our previous work [25,26]. The individual features are weighted equally in the global similarity function.…”