We have had to wait over 30 years since the naive Bayes model was first introduced in 1960
for the so-called Bayesian network classifiers to resurge. Based on Bayesian networks …

The purpose of this book is to provide an introduction to the theories and techniques of multi-
sensor data fusion. The book has been designed as a text for a one-semester graduate …

Due to the high availability of large-scale annotated image datasets, paramount progress
has been made in deep convolutional neural networks (CNNs) for image classification tasks …

Class-imbalanced datasets are common across several domains such as health, banking,
security, and others. The dominance of majority class instances (negative class) often results …

Two attributes $ A $ and $ B $ are said to interact when it helps to observe the attribute
values of both attributes together. This is an example of a $2 $-way interaction. In general, a …

Several oversampling methods have been proposed for solving the class imbalance
problem. However, most of them require searching the k-nearest neighbors to generate …

Attribute interactions are the irreducible dependencies between attributes. Interactions
underlie feature relevance and selection, the structure of joint probability and classification …

We explore the interplay of knowledge and structure in co-reference resolution. To inject
knowledge, we use a state-of-the-art system which cross-links (or “grounds”) expressions in …

This article proposes an improved Bayesian scheme by focusing on the region in which
Bayesian may fail to correctly identify labels and improve classification performance by …

Automated text categorization has witnessed a booming interest with the exponential growth
of information and the ever-increasing needs for organizations. The underlying hierarchical …