Inference and learning in hybrid probabilistic network

This paper proposed a novel hybrid probabilistic network, which is a good tradeoff between the model complexity and learnability in practice. It relaxes the conditional independence assumptions of Naive Bayes while still permitting efficient inference and learning. Experimental studies on a set of natural domains prove its clear advantages with respect to the generalization ability.

[1]  Igor Kononenko,et al.  Semi-Naive Bayesian Classifier , 1991, EWSL.

[2]  B. Silverman.  Density estimation for statistics and data analysis , 1986 .

[3]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[4]  James A. Hendler,et al.  Developing Hybrid Symbolic/Connectionist Models , 1991 .

[5]  Cheng-Lung Tseng,et al.  A self-growing probabilistic decision-based neural network with automatic data clustering , 2004, Neurocomputing.

[6]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[7]  Pat Langley,et al.  An Analysis of Bayesian Classifiers , 1992, AAAI.

[8]  Malgorzata Steinder,et al.  Probabilistic fault localization in communication systems using belief networks , 2004, IEEE/ACM Transactions on Networking.

[9]  Ron Sun,et al.  A hybrid model for learning sequential navigation , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[10]  Eamonn J. Keogh,et al.  Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches , 1999, AISTATS.

[11]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[12]  Nir Friedman,et al.  Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting , 1998, ICML.

[13]  C. D. Kemp,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[14]  Andrew Hunter,et al.  Feature Selection Using Probabilistic Neural Networks , 2000, Neural Computing & Applications.

[15]  Donald F. Specht,et al.  Probabilistic neural networks and general regression neural networks , 1996 .

[16]  Whei-Min Lin,et al.  Transformer-fault diagnosis by integrating field data and standard codes with training enhancible adaptive probabilistic network , 2005 .

[17]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[18]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.