An Evaluation of Neural Network Methods and Data Preparation Strategies for Novelty Detection (original) (raw)

An important issue in the design of a model for a particular data set is the quality of the data concerning the presence of anomalous observations (outliers) and their influence in the performance of pattern classifiers. Common approaches to deal with outliers remove them from data or improve the robustness of the machine learning method by handling outliers directly. We explore these two views by introducing a systematic methodology to compare the performance of neural methods applied to novelty detection. Firstly, we describe in a tutorial-like fashion the most common neural-based novelty detection techniques. Then, in order to compute reliable decision thresholds, we generalize the recent application of the bootstrap resampling technique to unsupervised novelty detection to the supervised case, and propose a outlier removal procedure based on it. Finally, we evaluate the performance of the neural network methods through simulations on a breast cancer data set, assessing their robustness to outliers and their sensitivity to training parameters, such as data scaling, number of neurons, training epochs and size of the training set. We conclude the paper by discussing the obtained results.

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