73,083 research outputs found
Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques
We are interested in forecasting bankruptcies in a probabilistic way. Specifically, we compare the classification performance of several statistical and machine-learning techniques, namely discriminant analysis (Altman's Z-score), logistic regression, least-squares support vector machines and different instances of Gaussian processes (GP's) -that is GP's classifiers, Bayesian Fisher discriminant and Warped GP's. Our contribution to the field of computational finance is to introduce GP's as a potentially competitive probabilistic framework for bankruptcy prediction. Data from the repository of information of the US Federal Deposit Insurance Corporation is used to test the predictions.Bankruptcy prediction, Artificial intelligence, Supervised learning, Gaussian processes, Z-score.
An Efficient Search Strategy for Aggregation and Discretization of Attributes of Bayesian Networks Using Minimum Description Length
Bayesian networks are convenient graphical expressions for high dimensional
probability distributions representing complex relationships between a large
number of random variables. They have been employed extensively in areas such
as bioinformatics, artificial intelligence, diagnosis, and risk management. The
recovery of the structure of a network from data is of prime importance for the
purposes of modeling, analysis, and prediction. Most recovery algorithms in the
literature assume either discrete of continuous but Gaussian data. For general
continuous data, discretization is usually employed but often destroys the very
structure one is out to recover. Friedman and Goldszmidt suggest an approach
based on the minimum description length principle that chooses a discretization
which preserves the information in the original data set, however it is one
which is difficult, if not impossible, to implement for even moderately sized
networks. In this paper we provide an extremely efficient search strategy which
allows one to use the Friedman and Goldszmidt discretization in practice
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