Extracting Discriminative Features Using Non-negative Matrix Factorization in Financial Distress Data

DOI: 10.1007/978-3-642-04921-7_55
Source: DBLP

ABSTRACT In the recent financial crisis the incidence of important cases of bankruptcy led to a growing interest in corporate bankruptcy
prediction models. In addition to building appropriate financial distress prediction models, it is also of extreme importance
to devise dimensionality reduction methods able to extract the most discriminative features. Here we show that Non-Negative
Matrix Factorization (NMF) is a powerful technique for successful extraction of features in this financial setting. NMF is
a technique that decomposes financial multivariate data into a few basis functions and encodings using non-negative constraints.
We propose an approach that first performs proper initialization of NMF taking into account original data using K-means clustering.
Second, builds a bankruptcy prediction model using the discriminative financial ratios extracted by NMF decomposition. Model
predictive accuracies evaluated in real database of French companies with statuses belonging to two classes (healthy and distressed)
are illustrated showing the effectiveness of our approach.

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    ABSTRACT: In recent years, Nonnegative Matrix Factorization (NMF) has become a popular model in data mining society. NMF aims to extract hidden patterns from a series of high-dimensional vectors automatically, and has been applied for dimensional reduction, unsupervised learning (clustering, semi-supervised clustering and co-clustering, etc.) and pre-diction successfully. This chapter surveys NMF in terms of the model for-mulation and its variations and extensions, algorithms and applications, as well as its relations with K-means and Probabilistic Latent Seman-tic Indexing (PLSI). In summary, we draw the following conclusions: 1) NMF has a good interpretability due to its nonnegative constraints; 2) NMF is very flexible regarding the choices of its objective functions and the algorithms employed to solve it; 3) NMF has a variety of applications; 4) NMF has a solid theoretical foundation and a close relationship with the existing state-of-the-art unsupervised learning models. However, as a new and developing technology, there are still many interesting open issues remained unsolved and waiting for research from theoretical and algorithmic perspectives.
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    01/2010; Springer., ISBN: 978-3-642-04532-5
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    ABSTRACT: Cost-sensitive learning is of critical importance in many domains including bankruptcy prediction where the costs of different errors are unequal. Most existing classification methods aim to minimize overall error based on the assumption that the costs are equal. This paper presents three cost-sensitive learning vector quantization (LVQ) approaches to incorporate cost matrix in classification. Experimental results on real-world data indicate the proposed approaches are effective alternatives for bankruptcy prediction in cost-sensitive situations.
    Computer Science and Information Technology, International Conference on. 01/2009;