Instance-Based Learning Algorithms
Machine Learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Data mining methods for knowledge discovery
Data mining methods for knowledge discovery
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Benefitting from the variables that variable selection discards
The Journal of Machine Learning Research
Overfitting in making comparisons between variable selection methods
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Pattern Analysis and Machine Intelligence
The problem of disguised missing data
ACM SIGKDD Explorations Newsletter
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Prototype based fuzzy classification in clinical proteomics
International Journal of Approximate Reasoning
A software package for interactive motor unit potential classification using fuzzy k-NN classifier
Computer Methods and Programs in Biomedicine
Adaptive prototype-based fuzzy classification
Fuzzy Sets and Systems
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Multiple-prototype classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Nearest prototype classification: clustering, genetic algorithms, or random search?
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Uniqueness of medical data mining
Artificial Intelligence in Medicine
Data mining a diabetic data warehouse
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Knowledge discovery approach to automated cardiac SPECT diagnosis
Artificial Intelligence in Medicine
Applying instance-based techniques to prediction of final outcome in acute stroke
Artificial Intelligence in Medicine
Fundamentals of clinical methodology
Artificial Intelligence in Medicine
Instance-based classifiers to discover the gradient of typicality in data
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Artificial Intelligence in Medicine
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Objective: The aim of this paper is to study the feasibility and the performance of some classifier systems belonging to family of instance-based (IB) learning as second-opinion diagnostic tools and as tools for the knowledge extraction phase in the process of knowledge discovery in clinical databases. Materials and methods: We consider three clinical databases: one relating to the differential diagnosis of erythemato-squamous diseases, the second to the diagnosis of the onset of diabetes mellitus and the third dealing with a problem of diagnostic imaging in nuclear cardiology. We apply five IB classifiers to each database; two are based on exemplars, one is based on prototypes and two are hybrid. One of the latter classifiers is a new classifier introduced here and is called prototype exemplar learning classifier (PEL-C). We use cross-validation techniques to evaluate and compare the performances of several classifier systems as diagnostic tools, considering indexes such as accuracy, sensitivity, specificity, and conciseness of class representations. Moreover we analyze the number and the type of instances that represent the diagnostic classes learnt by each classifier to evaluate and compare their knowledge extraction capabilities. Results: An examination of the experimental results shows that classifiers with the best classification performances are the optimized k-nearest neighbour classifier (k-NNC) and PEL-C. The k-NNC uses the highest number of representative instances, 100% of the entire database, whereas PEL-C uses a far lesser number of representative instances: equal, on the average, to the 3% of the database. As tools for knowledge extraction, we interpret the kind of class representations obtained by IB classifiers as a form of nosological knowledge. Additionally, we report the most interesting diagnostic class representations to be those extracted by PEL-C because they are composed of a mixture of abstracted prototypical cases (syndromes) and selected atypical clinical cases. Conclusion: This study shows that IB methods - most notably, the optimized k-NNC and the PEL-C - can be used and may be advantageous for clinical decision support systems and that IB classifiers can be used for nosological knowledge extraction. Because PEL-C uses more compact and potentially meaningful class descriptions, it is preferable when the diagnostic problem at-hand needs smaller storage space or for knowledge extraction itself. The complexity and responsibility of diagnostic practice requires that these results be confirmed further within other clinical domains.