Neural Networks: Computational Models and Applications (Studies in Computational Intelligence)
Neural Networks: Computational Models and Applications (Studies in Computational Intelligence)
Machine learning: a review of classification and combining techniques
Artificial Intelligence Review
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Missing values imputation techniques for neural networks patterns
ICS'08 Proceedings of the 12th WSEAS international conference on Systems
Dynamic Clustering-Based Estimation of Missing Values in Mixed Type Data
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Pattern classification with missing data: a review
Neural Computing and Applications - Special Issue - KES2008
GPU implementation of the multiple back-propagation algorithm
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Accurate prediction of financial distress of companies with machine learning algorithms
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
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Most of the classification algorithms (e.g. support vector machines, neural networks) cannot directly handle Missing Values (MV). A common practice is to rely on data pre-processing techniques by using imputation or simply by removing instances and/or features containing MV. This seems inadequate for various reasons: the resulting models do not preserve the uncertainty, these techniques might inject inaccurate values into the learning process, the resulting models are unable to deal with faulty sensors and data in real-world problems is often incomplete. In this paper we look at the Missing Values Problem (MVP) by extending our recently proposed Neural Selective Input Model (NSIM) first, to a novel multi-core architecture implementation and, second, by validating our method in a real-world financial application. The NSIM encompasses different transparent and bound (conceptual) models, according to the multiple combinations of missing attributes. The proposed NSIM is applied to bankruptcy prediction of (healthy and distressed) French companies, yielding much better performance than previous approaches using pre-processing techniques. Moreover, the Graphics Processing Unit (GPU) implementation reduces drastically the time spent in the learning phase, making the NSIM an excellent choice for dealing with the MVP.