Artificial Intelligence Review - Special issue on lazy learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Defining and Combining Symmetric and Asymmetric Similarity Measures
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Using Introspective Learning to Improve Retrieval in CBR: A Case Study in Air Traffic Control
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Similarity Metrics: A Formal Unification of Cardinal and Non-Cardinal Similarity Measures
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
IEA/AIE '98 Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial In telligence and Expert Systems: Tasks and Methods in Applied Artificial Intelligence
An Unsupervised Bayesian Distance Measure
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Foundations of Soft Case-Based Reasoning
Foundations of Soft Case-Based Reasoning
Data Mining for Case-Based Reasoning in High-Dimensional Biological Domains
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A case-based reasoning system for PCB principal process parameter identification
Expert Systems with Applications: An International Journal
A hierarchical approach for the redesign of chemical processes
Knowledge and Information Systems
A hybrid case adaptation approach for case-based reasoning
Applied Intelligence
Prototype-based classification
Applied Intelligence
A case-based approach for characterization and analysis of subgroup patterns
Applied Intelligence
Two stages of case-based reasoning - Integrating genetic algorithm with data mining mechanism
Expert Systems with Applications: An International Journal
A hybrid artificial immune system and Self Organising Map for network intrusion detection
Information Sciences: an International Journal
An association-based case reduction technique for case-based reasoning
Information Sciences: an International Journal
A hybrid intelligent system for fault detection and sensor fusion
Applied Soft Computing
A negative selection algorithm for classification and reduction of the noise effect
Applied Soft Computing
Ranking-order case-based reasoning for financial distress prediction
Knowledge-Based Systems
BAIS: A Bayesian Artificial Immune System for the effective handling of building blocks
Information Sciences: an International Journal
Information Sciences: an International Journal
Loss and gain functions for CBR retrieval
Information Sciences: an International Journal
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Knowledge and Information Systems
A forecasting solution to the oil spill problem based on a hybrid intelligent system
Information Sciences: an International Journal
Enhancing the classification accuracy by scatter-search-based ensemble approach
Applied Soft Computing
Engineering Applications of Artificial Intelligence
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Case-based reasoning (CBR), a popular problem solving methodology in data mining, solves new problems by analyzing solutions for similar past problems. The many advantages of CBR include rapid learning, the ability to use numerous unrestricted domains, minimal knowledge requirements, and effective presentation of knowledge. However, a major difficulty when applying CBR algorithms is selection of appropriate parameter values, features and weight assignment of features, to avoid constructing poor models. Unfortunately, key CBR parameters, beneficial features and the weight assignment of features vary across different problems. This study developed an efficient CBR approach based on artificial immune system algorithm (AISCBR) to increase classification accuracy by improving parameter tuning, feature selection and weight assignment of features. The proposed approach was then compared with those of other studies using the same University of California, Irvine (UCI) data sets. The experimental results showed that the AISCBR can provide better performance than other existing methods, because higher classification accurate rates can be obtained.