engleski

Noise detection and elimination in data preprocessing : experiments in medical domains

Compression measures used in inductive learners, such as measures based on the Minimum Description Length principle, can be used as a basis for grading candidate hypotheses. Compression-based induction is suited also for handling noisy data. This paper shows that a simple compression measure can be used to detect noisy training examples, where noise is due to random classification errors. A technique is proposed in which noisy examples are detected and eliminated from the training set, and a hypothesis is then built from the set of remaining examples. This noise elimination method was applied to preprocess data for four machine learning algorithms, and evaluated on selected medical domains.

machine learning; induction; noise detection

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