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This paper proposes a methodology for identifying data samples that are likely to be mislabeled in a c-class classification problem (dataset). The methodology relies on an assumption that the generalization error of a model learned from the data decreases if a label of some mislabeled sample is changed to its correct class. A general classification model used in the paper is OP-ELM; it also provides a fast way to estimate the generalization error by PRESS Leave-One-Out. It is tested on two toy datasets, as well as on real life datasets for one of which expert knowledge about the identified potential mislabels has been sought.

Long-term time series prediction is a difficult task. This is due to accumulation of errors and inherent uncertainties of a long-term prediction, which leads to deteriorated estimates of the future instances. In order to make accurate predictions, this paper presents a methodology that uses input processing before building the model. Input processing is a necessary step due to the curse of dimensionality, where the aim is to reduce the number of input variables or features. In the paper, we consider the combination of the delta test and the genetic algorithm to obtain two aspects of reduction: scaling and projection. After input processing, two fast models are used to make the predictions: optimally pruned extreme learning machine and optimally pruned k-nearest neighbors. Both models have fast training times, which makes them suitable choice for direct strategy for long-term prediction. The methodology is tested on three different data sets: two time series competition data sets and one financial data set.

This article deals with the usefulness of self organizing maps in the area of corporate finance. The application of neural networks improve bankruptcy forecasting by showing a relationship between capital structure and corporate performance. Our results suggest the pertinence of the Kohonen algorithm applied to qualitative variables. These results allow us to question scoring models. In a larger framework, the methodology of Kohonen allowed a better perception of the factors able to explain the financing of leasing. The objective of our research is here to explain the factors of the choice between leasing and banking loans.