[PDF] Improving classification accuracy by identifying and removing instances that should be misclassified | Semantic Scholar

This paper examines C4.5, a decision tree algorithm that is already quite robust - few algorithms have been shown to consistently achieve higher accuracy, and extends the pruning method to fully remove the effect of outliers, and this results in improvement on many databases.

This paper uses a set of learning algorithms to create classifiers that serve as noise filters for the training data and suggests that for situations in which there is a paucity of data, consensus filters are preferred, whereas majority vote filters are preferable for situations with an abundance of data.

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This paper presents a novel approach to outlier detection based on classification, which is superior to other methods based on the same reduction to classification, but using standard classification methods, and shows that it is competitive to the state-of-the-art outlier Detection methods in the literature.