Accession Number:

ADA614917

Title:

Dynamic Dimensionality Selection for Bayesian Classifier Ensembles

Descriptive Note:

Final rept. 1 Apr 2012-11 Mar 2015

Corporate Author:

MONASH UNIV VICTORIA (AUSTRALIA)

Personal Author(s):

Report Date:

2015-03-19

Pagination or Media Count:

25.0

Abstract:

This project seeked to develop new learning algorithms specifically tailored to be efficient and effective in learning from big data. It exploited the capacity of generative learning to efficiently extract useful summary statistics and used discriminative learning to meld them into a highly accurate classifier. Two classes of learning algorithm were developed. The first uses discriminative learning to select a generative model selective ANDE and selective KDB. Very effective feature selection was achieved with a single pass through the training data for each attribute that is finally selected. The second combines generatively and discriminatively learned parameters WANBIA, WANBIA-C,WANJE. It uses discriminative learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very competitive to Logistic Regression but much more efficient in learning the model. WNANJE can model higher-order attribute interdependencies.

Subject Categories:

  • Numerical Mathematics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE