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.
Similar to the preference-based similarity measure development framework presented by authors in @cite_1 @cite_3 , we are presenting a framework for modelling local similarity measures based on the data set available. Therewith we can tailor each similarity measure to the application domain. Using a data-driven approach for automatic similarity learning and feature weighting has been presented by Gabel and Godehardt @cite_5 where they trained a neural network to induce local and global similarity measures @cite_2 . While we are not automatically assigning the similarity measures, we use the existing cases to derive them.
@cite_5 @cite_1 @cite_3 @cite_2
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The automatic acquisition of a similarity measure for a CBR system is appealing as it frees the system designer from the tedious task of defining it manually. However, acquiring similarity measures with some machine learning approach typically results in some black box representation of similarity whose magic-like combination of high precision and low explainability may decrease a human user’s trust in the system. In this paper, we target this problem by suggesting a method to induce a human-readable and easily understandable – and thus potentially trustworthy – representation of similarity from a previously learned black box-like representation of similarity measures. Our experimental evaluations support the claim that, given some highly precise learned similarity measure, we can induce a less powerful, but human-understandable representation of it while its corresponding level of accuracy is only marginally impaired. Building on recent research on preference handling in artificial intelligence and related fields, our general goal is to develop a coherent and universally applicable methodological framework for CBR on the basis of formal concepts and methods for knowledge representation and reasoning with preferences. A preference-based approach to CBR appears to be appealing for several reasons, notably because case-based experiences naturally lend themselves to representations in terms of preference relations, even when not dealing with preference information in a literal sense. Moreover, the flexibility and expressiveness of a preference-based formalism well accommodate the uncertain and approximate nature of case-based problem solving. In this paper, we make a first step toward a preference-based formalization of CBR. Apart from providing a general outline of the framework as a whole, we specifically address the step of case-based inference. The latter consists of inferring preferences for candidate solutions in the context of a new problem, given such preferences in similar situations. Our case-based approach to predicting preference models is concretely realized for a scenario in which solutions are represented in the form of subsets of a reference set. First experimental results are presented to demonstrate the effectiveness of this approach. This paper is a continuation of our recent work on preference-based CBR, or Pref-CBR for short. The latter is conceived as a case-based reasoning methodology in which problem solving experience is represented in the form of contextualized preferences, namely preferences for candidate solutions in the context of a target problem to be solved. In our Pref-CBR framework, case-based problem solving is formalized as a preference-guided search process in the space of candidate solutions, which is equipped with a similarity (or, equivalently, a distance) measure. Since the efficacy of Pref-CBR is influenced by the adequacy of this measure, we propose a learning method for adapting solution similarity on the basis of experience gathered by the CBR system in the course of time. More specifically, our method makes use of an underlying probabilistic model and realizes adaptation as Bayesian inference. The effectiveness of this method is illustrated in a case study that deals with the case-based recommendation of red wines. Of which nature is the knowledge a similarity measure can contain? How to bring the knowledge into the measure? How to retrieve and use the knowledge for actual problems?