Communications of the ACM
Proc. of the fifth technical conference of the British Computer Society Specialist Group on Expert Systems on Expert systems 85
Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Learning decision rules in noisy domains
Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Concept Acquisition in Noisy Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifier systems and genetic algorithms
Machine learning: paradigms and methods
Performance analysis of a probabilistic inductive learning system
Proceedings of the seventh international conference (1990) on Machine learning
Learning from data with bounded inconsistency
Proceedings of the seventh international conference (1990) on Machine learning
Learning classification rules using Bayes
Proceedings of the sixth international workshop on Machine learning
The induction of probabilistic rule sets—the Itrule algorithm
Proceedings of the sixth international workshop on Machine learning
Induction of decision trees from inconclusive data
Proceedings of the sixth international workshop on Machine learning
Imprecise concept learning within a growing language
Proceedings of the sixth international workshop on Machine learning
Knowledge base refinement as improving an incorrect, inconsistent and incomplete domain theory
Proceedings of the sixth international workshop on Machine learning
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Machine learning
A case-based apprentice that learns from fuzzy examples
Methodologies for intelligent systems, 5
Learning to predict in uncertain continuous tasks
ML92 Proceedings of the ninth international workshop on Machine learning
Some approaches to handle noise in concept learning
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
C4.5: programs for machine learning
C4.5: programs for machine learning
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Managing Uncertainty in Expert Systems
Managing Uncertainty in Expert Systems
Machine Learning
Machine Learning
Machine Learning
Incremental Learning from Noisy Data
Machine Learning
Machine Learning
On Changing Continuous Attributes into Ordered Discrete Attributes
EWSL '91 Proceedings of the European Working Session on Machine Learning
On Estimating Probabilities in Tree Pruning
EWSL '91 Proceedings of the European Working Session on Machine Learning
Bayes and Pseudo-Bayes Estimates of Conditional Probabilities and Their Reliability
ECML '93 Proceedings of the European Conference on Machine Learning
Induction of Recursive Bayesian Classifiers
ECML '93 Proceedings of the European Conference on Machine Learning
Decision Tree Pruning as a Search in the State Space
ECML '93 Proceedings of the European Conference on Machine Learning
Handling Various Types of Uncertainty in the Rough Set Approach
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
On the Unknown Attribute Values in Learning from Examples
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Noise-tolerant instance-based learning algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Noise and knowledge acquisition
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
The More We Learn the Less We Know?: On Inductive Learning from Examples
Fundamenta Informaticae
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Very frequently machine learning from real-life data is affected by uncertainty. There are three main reasons for imperfection in data: incompleteness, imprecision (also called vagueness), and errors. In this paper the main emphasis is on classification of unseen examples using a rule set induced from imperfect data. The classification strategy of the machine learning system LERS is described in detail. Results of experiments with medical data sets are also reported.