Learning Solution Similarity in Preference-Based CBR

Abdel-Aziz, Amira; Strickert, Marc; Hüllermeier, Eyke

doi:10.1007/978-3-319-11209-1_3

Cited by 8 publications

(10 citation statements)

References 12 publications

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“…Section 5 shows that all of the learned similarity measures outperformed the classical similarity measure t 1,1 and also t 2,1 where the local (per feature) similarity measures were adapted to the statistical properties of the features [1]. In practice, one should weight the importance of each feature according to how important it is in terms of similarity measurement.…”

Section: Discussionmentioning

confidence: 99%

“…Abdel-Aziz et al [1] used the distribution of case attribute values to inform a polynomial local similarity function, which is better than guessing when domain knowledge is missing. So this method extracts statistical properties from the dataset to parametrize C(x,ŷ).…”

Section: Related Workmentioning

confidence: 99%

“…In t 1,1 , all weights and local similarity measures are uniformly distributed and not parametrized by the data. We extended this with a Type 2 similarity measure t 2,1 , which is based on the work from Abdel-Aziz et al [1], where the local similarity measures are parametrized by the data from the corresponding features.…”

Section: Reference Similarity Measuresmentioning

confidence: 99%

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Learning similarity measures from data

et al. 2019

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Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or a set of cases most similar to the query case. Describing a similarity measure analytically is challenging, even for domain experts working with CBR experts. However, datasets are typically gathered as part of constructing a CBR or machine learning system. These datasets are assumed to contain the features that correctly identify the solution from the problem features; thus, they may also contain the knowledge to construct or learn such a similarity measure. The main motivation for this work is to automate the construction of similarity measures using machine learning. Additionally, we would like to do this while keeping training time as low as possible. Working toward this, our objective is to investigate how to apply machine learning to effectively learn a similarity measure. Such a learned similarity measure could be used for CBR systems, but also for clustering data in semi-supervised learning, or one-shot learning tasks. Recent work has advanced toward this goal which relies on either very long training times or manually modeling parts of the similarity measure. We created a framework to help us analyze the current methods for learning similarity measures. This analysis resulted in two novel similarity measure designs: The first design uses a pre-trained classifier as basis for a similarity measure, and the second design uses as little modeling as possible while learning the similarity measure from data and keeping training time low. Both similarity measures were evaluated on 14 different datasets. The evaluation shows that using a classifier as basis for a similarity measure gives state-of-the-art performance. Finally, the evaluation shows that our fully data-driven similarity measure design outperforms state-of-the-art methods while keeping training time low.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Learning similarity measures from data

et al. 2019

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“…Similar to the preference-based similarity measure development framework presented by authors in [4,1], 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.…”

Section: Related Workmentioning

confidence: 99%

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

Verma

Bach

Mork

2019

Communications in Computer and Information Science

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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.

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“…In paper [22] similarity measures have been learned using feedback and similarity teacher. Local similarity measures as well as the learning of comprehend similarity measures have been obtained using Artificial Neural Networks is presented in [12,2].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Approach for Determining Weights in Global Similarity Functions

Jaiswal

Bach

2019

Case-Based Reasoning Research and Development

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This paper presents a method to discover initial global similarity weights while developing a case-based reasoning (CBR) system. The approach is based on multiple feature relevance scoring methods and the relevance of features within each scoring method. The objective of this work is to utilize the characteristics of a dataset when creating similarity measures. The primary advantage of this method lies in its data-driven approach in the absence of domain knowledge in the early phase of a CBR system development. The results obtained based on the experiments on multiple public datasets show that the method improves the performance of similarity measures for a CBR system in discriminating relevant similar cases. Evaluation of the results is based on the method suitable for unbalanced datasets.

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Learning Solution Similarity in Preference-Based CBR

Cited by 8 publications

References 12 publications

Learning similarity measures from data

Learning similarity measures from data

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

A Data-Driven Approach for Determining Weights in Global Similarity Functions

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