Classifier chain (CC) is a multi-label learning approach that constructs sequence of binary classifiers according to label order. Each classifier in the responsible for predicting relevance one label. When training label, proceeding labels will be taken as extended features. If features are highly correlated performance improved, otherwise, not influenced or even degraded. How discover correlat...

LBR has demonstrated outstanding classification accuracy. However, it has high computational overheads when large numbers of instances are classified from a single training set. We compare LBR and the tree-augmented Bayesian classifier, and present a new heuristic LBR classifier that combines elements of the two. It requires less computation than LBR, but demonstrates similar prediction accuracy.

A face detection system is presented. A new classification method using foreststructured Bayesian networks is used. The method is used in an aggregated classifier to discriminate face from non-face patterns. The process of generating non-face patterns is integrated with the construction of the aggregated classifier. The face detection system performs well in comparison with other well-known met...

Bayesian network classifiers are used in many fields, and one common class of classifiers are naive Bayes classifiers. In this paper, we introduce an approach for reasoning about Bayesian network classifiers in which we explicitly convert them into Ordered Decision Diagrams (ODDs), which are then used to reason about the properties of these classifiers. Specifically, we present an algorithm for...

We introduce a Bayesian network classifier less restrictive than Naive Bayes (NB) and Tree Augmented Naive Bayes (TAN) classifiers. Considering that learning an unrestricted network is unfeasible the proposed classifier is confined to be consistent with the breadth-first search order of an optimal TAN. We propose an efficient algorithm to learn such classifiers for any score that decompose over...

Many collaborative filtering enabled Web sites that recommend books, CDs, movies, videos and so on, have become very popular on Internet. They recommend items to a user based on the opinions of other users with similar tastes. In this paper, we discuss an approach to collaborative filtering based on the simple Bayesian classifier. The simple Bayesian classifier is one of the most successful sup...

This paper describes a crowdsourcing system that integrates machine learning techniques with human classifiers, showing how to apply a Bayesian approach to classifier combination to the challenge of crowdsourcing document topic labels. First, we use a number of NLP techniques to extract informative document features. We then screen and select workers using Amazon Mechanical Turk to label select...

Linear discriminant analysis (LDA) has been widely used for dimension reduction of data sets with multiple classes. The LDA has been recently extended to various generalized LDA methods which are applicable regardless of the relative sizes between the data dimension and the number of data items. In this paper, we propose several multiclass classifiers based on generalized LDA algorithms, taking...