DEFECT PREDICTION USING CASE-BASED REASONING: AN ATTRIBUTE WEIGHTING TECHNIQUE BASED UPON SENSITIVITY ANALYSIS IN NEURAL NETWORKS

2012 ◽  
Vol 22 (06) ◽  
pp. 747-768 ◽  
Author(s):  
ELHAM PAIKARI ◽  
MICHAEL M. RICHTER ◽  
GUENTHER RUHE
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Absolute Error ◽  
Defect Prediction ◽  
Case Based Reasoning ◽  
Data Sets ◽  
Attribute Weighting ◽  
Weight Calculation ◽  
The Impact ◽  
Case Based

Software defect prediction is an acknowledged approach used to achieve better product quality and to better utilize resources needed for that purpose. One known method for predicting the number of defects is to apply case-based reasoning (CBR). In this paper, different attribute weighting techniques for CBR-based defect prediction are analyzed. One of the weighting techniques used in this work, Sensitivity Analysis based on Neural Networks (SANN), is based on sensitivity analysis of the impact of attributes as part of neural network analysis. Neural networks are applicable when there are non-linear and complicated relationships among the attributes. Since weighting plays a key role in the CBR model, using an efficient weight calculation method can change the results. The results of SANN are compared with applying uniform weights and weights gained from Multiple Linear Regression (MLR).Evaluation of the accuracy of the overall method for applying the three different weighting techniques is done over five data sets, comprising about 5000 modules from NASA. Two quality measures are applied: Average Absolute Error (AAE) and Average Relative Error (ARE). In addition to the variation of weighting techniques, the impact of varying the number of nearest neighbors is studied.The three main results of the empirical analysis are: (i) In the majority of cases, SANN achieves the most accurate results; (ii) uniform weighting performs better than the MLR-based weighting heuristic; and (iii) there is no significant preference pattern for defining the number of similar objects used for prediction in CBR.

scholarly journals Using extended siamese networks to provide decision support in aquaculture operations

2021 ◽  
Author(s):  
Bjørn Magnus Mathisen ◽  
Kerstin Bach ◽  
Agnar Aamodt
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Similar Case ◽  
Decision Makers ◽  
Similarity Function ◽  
Case Based Reasoning ◽  
Similarity Learning ◽  
Reasoning System ◽  
Siamese Networks ◽  
Case Based

AbstractAquaculture as an industry is quickly expanding. As a result, new aquaculture sites are being established at more exposed locations previously deemed unfit because they are more difficult and resource demanding to safely operate than are traditional sites. To help the industry deal with these challenges, we have developed a decision support system to support decision makers in establishing better plans and make decisions that facilitate operating these sites in an optimal manner. We propose a case-based reasoning system called aquaculture case-based reasoning (AQCBR), which is able to predict the success of an aquaculture operation at a specific site, based on previously applied and recorded cases. In particular, AQCBR is trained to learn a similarity function between recorded operational situations/cases and use the most similar case to provide explanation-by-example information for its predictions. The novelty of AQCBR is that it uses extended Siamese neural networks to learn the similarity between cases. Our extensive experimental evaluation shows that extended Siamese neural networks outperform state-of-the-art methods for similarity learning in this task, demonstrating the effectiveness and the feasibility of our approach.

A COMPARISON OF SCORING METRICS FOR PREDICTING THE NEXT NAVIGATION STEP WITH MARKOV MODEL-BASED SYSTEMS

2010 ◽  
Vol 09 (04) ◽  
pp. 547-573 ◽  
Author(s):  
JOSÉ BORGES ◽  
MARK LEVENE
Keyword(s):  
Markov Model ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Markov Models ◽  
Real Data ◽  
Absolute Error ◽  
Brier Score ◽  
Data Sets ◽  
Extensive Evaluation ◽  
The Impact

The problem of predicting the next request during a user's navigation session has been extensively studied. In this context, higher-order Markov models have been widely used to model navigation sessions and to predict the next navigation step, while prediction accuracy has been mainly evaluated with the hit and miss score. We claim that this score, although useful, is not sufficient for evaluating next link prediction models with the aim of finding a sufficient order of the model, the size of a recommendation set, and assessing the impact of unexpected events on the prediction accuracy. Herein, we make use of a variable length Markov model to compare the usefulness of three alternatives to the hit and miss score: the Mean Absolute Error, the Ignorance Score, and the Brier score. We present an extensive evaluation of the methods on real data sets and a comprehensive comparison of the scoring methods.

scholarly journals Performance Evaluation of Distance Measurement Methods for Construction Noise Prediction Using Case-Based Reasoning

2019 ◽  
Vol 11 (3) ◽  
pp. 871 ◽  
Author(s):  
Nahyun Kwon ◽  
Joosung Lee ◽  
Moonsun Park ◽  
Inseok Yoon ◽  
Yonghan Ahn
Keyword(s):  
Absolute Error ◽  
Distance Measurement ◽  
Error Rates ◽  
Measurement Methods ◽  
Case Based Reasoning ◽  
Noise Prediction ◽  
Cost Overruns ◽  
Construction Firms ◽  
Sustainable Environment ◽  
Case Based

Concerns over environmental issues have recently increased. Particularly, construction noise in highly populated areas is recognized as a serious stressor that not only negatively affects humans and their environment, but also construction firms through project delays and cost overruns. To deal with noise-related problems, noise levels need to be predicted during the preconstruction phase. Case-based reasoning (CBR) has recently been applied to noise prediction, but some challenges remain to be addressed. In particular, problems with the distance measurement method have been recognized as a recurring issue. In this research, the accuracy of the prediction results was examined for two distance measurement methods: The weighted Euclidean distance (WED) and a combination of the Jaccard and Euclidean distances (JED). The differences and absolute error rates confirmed that the JED provided slightly more accurate results than the WED with an error ratio of approximately 6%. The results showed that different methods, depending on the attribute types, need to be employed when computing similarity distances. This research not only contributes an approach to achieve reliable prediction with CBR, but also contributes to the literature on noise management to ensure a sustainable environment by elucidating the effects of distance measurement depending on the attribute types.

Research of Case Retrieval Strategy in Case-Based Reasoning

2013 ◽  
Vol 278-280 ◽  
pp. 2016-2019 ◽  
Author(s):  
Jian Hua Song ◽  
Zheng Wang ◽  
Lei Zhang
Keyword(s):  
Case Based Reasoning ◽  
Retrieval Strategy ◽  
Case Retrieval ◽  
Matching Algorithm ◽  
Field Matching ◽  
The Times ◽  
The Impact ◽  
Case Based

This paper put forward a new retrieval strategy which combines character field matching algorithm with the NNH in Case-Based Reasoning. The new retrieval strategy can reduce the times of symptoms match while streamlining retrieved result, and lower the impact of large symptom value difference in the result. The superiority of the strategy is verified by three target cases.

scholarly journals Approximate case-based reasoning on neural networks

1994 ◽  
Vol 10 (1) ◽  
pp. 75-98 ◽  
Author(s):  
Zuliang Shen ◽  
Ho Chung Lui ◽  
Liya Ding
Keyword(s):  
Neural Networks ◽  
Case Based Reasoning ◽  
Case Based
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