An optimal formulation of feature weight allocation for CBR using machine learning techniques

Tamoor, Maria; Gül, Hina; Qaiser, Hafsah; Ali, Amna

doi:10.1109/intellisys.2015.7361085

Search citation statements

Order By: Relevance

Paper Sections

Select...

Related Work1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2019

Publication Types

Select...

Book1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multiple papers have presented the evaluation of feature weights based on mean absolute error, mean squared error, or accuracy [25,21,23]. However, when datasets are unbalanced, these evaluation methods might not be suitable, due to the well known issues of class-imbalance and accuracy paradox [26].…”

Section: Related Workmentioning

confidence: 99%

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

Jaiswal

Bach

2019

Case-Based Reasoning Research and Development

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%