Bagging k-dependence Bayesian network classifiers

Bagging has attracted much attention due to its simple implementation and the popularity of bootstrapping. By learning diverse classifiers from resampled datasets and averaging the outcomes, bagging investigates the possibility of achieving substantial classification performance of the base classifier. Diversity has been recognized as a very important characteristic in bagging. This paper presents an efficient and effective bagging approach, that learns a set of independent Bayesian network classifiers (BNCs) from disjoint data subspaces. The number of bits needed to describe the data is measured in terms of log likelihood, and redundant edges are identified to optimize the topologies of the learned BNCs. Our extensive experimental evaluation on 54 publicly available datasets from the UCI machine learning repository reveals that the proposed algorithm achieves a competitive classification performance compared with state-of-the-art BNCs that use or do not use bagging procedures, such as tree-augmented naive Bayes (TAN), k-dependence Bayesian classifier (KDB), bagging NB or bagging TAN.

A Kind of Combination Feature Division and Diversity Measure of Multi-Classifier Selective Ensemble Algorithm

In ensemble learning, in order to improve the performance of individual classifiers and the diversity of classifiers, from the classifiers generation and combination, this paper proposes a kind of combination feature division and diversity measure of multi-classifier selective ensemble algorithm. The algorithm firstly applied bagging method to create some feature subsets, Secondly using principal component analysis of feature extraction method on each feature subsets, then select classifiers with high-classification accuracy; finally before classifier combination we use classifier diversity measure method select diversity classifiers. Experimental results prove that classification accuracy of the algorithm is obviously higher than popular bagging algorithm.