Feature Weighting and Confidence Based Prediction for Case Based Reasoning Systems

Kar, Debarun; Chakraborti, Sutanu; Ravindran, Balaraman

doi:10.1007/978-3-642-32986-9_17

Cited by 12 publications

(7 citation statements)

References 11 publications

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“…This research using medical record of hepatitis patient from previous research [8]. Numbers of cases are 117, where each case has 52 attributes.…”

Section: B Data and Testingmentioning

confidence: 99%

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

Bagi¹,

Suprapto

2018

JISEBI

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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 were four variations of case base size and test data used in the research; i.e., 50% of case base and 50% of test data, 60% of case base and 40% of test data, 70% of case base and 30% of test data, and 80% of case base and 20% of test data were variation 1, 2, 3, and 4 respectively. In addition, each variation used four scenarios based on the way how to mark the test data. In scenario 1, 2, 3 and 4, the test data were respectively marked at the end, the beginning, the half of beginning and half of end, and the middle. The result 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 bigger chance for the system to find similar cases.

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“…This research using medical record of hepatitis patient from previous research [8]. Numbers of cases are 117, where each case has 52 attributes.…”

Section: B Data and Testingmentioning

confidence: 99%

“…The results of the CB-FDT performance test showed that average accuracy was 99.5% in breast cancer and 85% in liver disease. There is a research about CBR for diagnosis of hepatitis disease using medical record data of hepatitis patients [8]. In this research, feature weight assigned by expert and accuracy of the system was 94.29%.…”

Section: Introductionmentioning

confidence: 96%

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

Bagi¹,

Suprapto

2018

JISEBI

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“…It is noted that the model weight is case dependent in the CBR. The CBR strategy has been successfully applied to combine multiple protein-ligand interaction models for docking scoring, and significantly improve the performance of high-throughput screening (71). The challenge for CBR is how to select relevant features and how to assess the similarity between cases.…”

Section: Model Integrationmentioning

confidence: 99%

Harnessing Big Data for Systems Pharmacology

Xie

Draizen

Bourne

2016

Preprint

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Systems pharmacology aims to holistically understand genetic, molecular, cellular, organismal, and environmental mechanisms of drug actions through developing mechanistic or predictive models. Data-driven modeling plays a central role in systems pharmacology, and has already enabled biologists to generate novel hypotheses. However, more is needed. The drug response is associated with genetic/epigenetic variants and environmental factors, is coupled with molecular conformational dynamics, is affected by possible off-targets, is modulated by the complex interplay of biological networks, and is dependent on pharmacokinetics. Thus, in order to gain a comprehensive understanding of drug actions, systems pharmacology requires integration of models across data modalities, methodologies, organismal hierarchies, and species. This imposes a great challenge on model management, integration, and translation. Here, we discuss several upcoming issues in systems pharmacology and potential solutions to them using big data technology. It will allow systems pharmacology modeling to be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable.

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“…It has proved that 3-layer MFNN can realize any given function for approximate accuracy, thus it can be used to solve the nonlinear classification problem. The case library in the CBR system can be viewed as a CSP, therefore CS-ANN model, such as Schema model, Hopfield model, Boltzmann and Harmony theory can be employed to construct the case library [6,7].…”

Section: Case Intelligence With Mfnnmentioning

confidence: 99%

CBR-Recommendation System on Massive Contents Processing Using Optimized MFNN Algorithm

Li¹,

Li²,

Zhu³

2015

Proceedings of the 2015 International Symposium on Computers &Amp; Informatics

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Though recommendation systems have been widely used for websites to generate new recommendations based on like-minded users' preferences, IEEE Internet Computing points out that current system can not meet the real large-scale e-commerce demands, and has some weakness such as low precision and slow reaction. Huge personalized data are the key to successfully give a new recommendation, but they are difficultly dealt with for they are massive with high dimensional; addressing such problems, the paper suggests to use multi-layer feed-forward neural networks (MFNN) system based on case intelligence to partition massive personalized data into the most similar groups. The subsequent experiment indicates that our system model is constructive and understandable, and our algorithm can decrease the complexity of ANN algorithm, for which the system performance can be guaranteed.

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Feature Weighting and Confidence Based Prediction for Case Based Reasoning Systems

Cited by 12 publications

References 11 publications

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

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

Harnessing Big Data for Systems Pharmacology

CBR-Recommendation System on Massive Contents Processing Using Optimized MFNN Algorithm

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