Feature weighting by explaining case-based planning episodes

1996 ◽  
pp. 280-294 ◽  
Author(s):  
Héctor Muñoz-Avila ◽  
Jochem Hüllen
Keyword(s):  
Feature Weighting ◽  
Case Based

A learning algorithm for feature weighting in case-based reasoning

2012 ◽  
Vol 2 (2/3) ◽  
pp. 160
Author(s):  
Maryam Ghadami ◽  
Seyed Mohammad Seyedhosseini ◽  
Ahmad Makui
Keyword(s):  
Learning Algorithm ◽  
Feature Weighting ◽  
Case Based Reasoning ◽  
Case Based

scholarly journals Twin-Systems to Explain Artificial Neural Networks using Case-Based Reasoning: Comparative Tests of Feature-Weighting Methods in ANN-CBR Twins for XAI

2019 ◽  
Author(s):  
Eoin M. Kenny ◽  
Mark T. Keane
Keyword(s):  
Neural Networks ◽  
Evaluation Method ◽  
Nearest Neighbor ◽  
Image Data ◽  
Black Box ◽  
Feature Weighting ◽  
Case Based Reasoning ◽  
Artificial Neural ◽  
Weighting Methods ◽  
Case Based

In this paper, twin-systems are described to address the eXplainable artificial intelligence (XAI) problem, where a black box model is mapped to a white box “twin” that is more interpretable, with both systems using the same dataset. The framework is instantiated by twinning an artificial neural network (ANN; black box) with a case-based reasoning system (CBR; white box), and mapping the feature weights from the former to the latter to find cases that explain the ANN’s outputs. Using a novel evaluation method, the effectiveness of this twin-system approach is demonstrated by showing that nearest neighbor cases can be found to match the ANN predictions for benchmark datasets. Several feature-weighting methods are competitively tested in two experiments, including our novel, contributions-based method (called COLE) that is found to perform best. The tests consider the ”twinning” of traditional multilayer perceptron (MLP) networks and convolutional neural networks (CNN) with CBR systems. For the CNNs trained on image data, qualitative evidence shows that cases provide plausible explanations for the CNN’s classifications.

MBNR: Case-Based Reasoning with Local Feature Weighting by Neural Network

2004 ◽  
Vol 21 (3) ◽  
pp. 265-276 ◽  
Author(s):  
Jae Heon Park ◽  
Kwang Hyuk Im ◽  
Chung-Kwan Shin ◽  
Sang Chan Park
Keyword(s):  
Neural Network ◽  
Feature Weighting ◽  
Local Feature ◽  
Case Based Reasoning ◽  
Case Based

scholarly journals Hepatitis Diagnosis Using Case-Based Reasoning with Gradient Descent as Feature Weighting Method

2018 ◽  
Vol 4 (1) ◽  
pp. 25
Author(s):  
Yufika Sari Bagi ◽  
Suprapto Suprapto
Keyword(s):  
Test Data ◽  
Gradient Descent ◽  
Measurement Techniques ◽  
Feature Weighting ◽  
Similarity Measurement ◽  
Case Based Reasoning ◽  
Weighting Method ◽  
Feature Weights ◽  
Case Base ◽  
Case Based

Retrieval is one of the stages in case-based reasoning system which find a solution to new problem or case by measuring the similarity between the new case and old cases in the case base. Some of the similarity measurement techniques are involving feature weights that show the importance of the feature in a case. Feature weights can be obtained from a domain expert or by using a feature weighting method either locally or globally. Gradient descent is the feature weighting method which computes global weights for each feature. This research implemented gradient descent to obtain feature weights in case-based reasoning for hepatitis diagnosis and the similarity measurement using weighted Euclidean distance. There are four variations number of case base and test data that used in this research, those are: the first variation using 50% of data as case base and 50% as test data second variation using 60% of data as case base and 40% as test data, third variation using 70% of data as case base and 30% as test data and fourth variation using 80% of data as case base and 20% as test data. For each variation, using 4 kinds of scenario to mark the test data those are in first scenario the test data mark at the end of data, in second scenario the test data mark at the begin of data, in third scenario the test data mark half at the begin and half at the end of data and in the fourth scenario the test data mark in the middle of data. The result of this research showed that the accuracy of the system reaches 100% at scenario 1 in variation 4. Overall of all four variations and four kinds of scenario, the average accuracy of the system was 77.55%, average recall of system was 69.74%, and the average of precision was 78.39%. In addition, the level of accuracy was also influenced by the number of case base and the scenario of case selection for the case base. This is because more cases in the case base, the chances of a system to finding similar cases will be more.

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