Abstract
As was shown recently [19], prior knowledge has a significant importance in machine learning from the no free lunch theorem viewpoint. One of the type of prior information for classification task is knowledge on the data. Here we will propose another type of prior knowledge, for which a distance from decision boundary to selected data samples (detractors) is maximised. Support Vector Machines (SVMs) is a widely used algorithm for data classification. Detractors will be incorporated into SVMs by weighting the samples. For the reason, that standard C-SVM sample weights are not suitable for maximising the distance to selected points, additional SVM weights will be proposed.We will show that detractors can enhance the classification quality for areas with lack of training samples and for time series classification.We will demonstrate that incorporating detractors to stock price predictive models can lead to increased investment profits.
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Orchel, M. (2009). Incorporating Detractors into SVM Classification. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds) Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00563-3_38
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DOI: https://doi.org/10.1007/978-3-642-00563-3_38
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