Inductive knowledge acquisition: a case study
Proceedings of the Second Australian Conference on Applications of expert systems
The nature of statistical learning theory
The nature of statistical learning theory
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Artificial Intelligence Review - Special issue on lazy learning
Control-Sensitive Feature Selection for Lazy Learners
Artificial Intelligence Review - Special issue on lazy learning
Lazy learning
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Examining Locally Varying Weights for Nearest Neighbor Algorithms
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
ROSE - Software Implementation of the Rough Set Theory
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Feature Selection Via Mathematical Programming
INFORMS Journal on Computing
Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On Active Learning for Data Acquisition
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
The art of artificial intelligence: I. Themes and case studies of knowledge engineering
The art of artificial intelligence: I. Themes and case studies of knowledge engineering
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Decision trees with minimal costs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
MBNR: Case-Based Reasoning with Local Feature Weighting by Neural Network
Applied Intelligence
Active Feature-Value Acquisition for Classifier Induction
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Predicting good probabilities with supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Journal of Biomedical Informatics
Active Feature-Value Acquisition
Management Science
Journal of Artificial Intelligence Research
Using global properties for qualitative reasoning: a qualitative system theory
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Learning and classifying under hard budgets
ECML'05 Proceedings of the 16th European conference on Machine Learning
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
Artificial Intelligence in Medicine
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Objective: Speed, cost, and accuracy are three important goals in disease diagnosis. This paper proposes a machine learning-based expert system algorithm to optimize these goals and assist diagnostic decisions in a sequential decision-making setting. Methods: The algorithm consists of three components that work together to identify the sequence of diagnostic tests that attains the treatment or no test threshold probability for a query case with adequate certainty: lazy-learning classifiers, confident diagnosis, and locally sequential feature selection (LSFS). Speed-based and cost-based objective functions can be used as criteria to select tests. Results: Results of four different datasets are consistent. All LSFS functions significantly reduce tests and costs. Average cost savings for heart disease, thyroid disease, diabetes, and hepatitis datasets are 50%, 57%, 22%, and 34%, respectively. Average test savings are 55%, 73%, 24%, and 39%, respectively. Accuracies are similar to or better than the baseline (the classifier that uses all available tests in the dataset). Conclusion: We have demonstrated a new approach that dynamically estimates and determines the optimal sequence of tests that provides the most information (or disease probability) based on a patient's available information.