5,196 research outputs found
Evaluation of the Performance of the Markov Blanket Bayesian Classifier Algorithm
The Markov Blanket Bayesian Classifier is a recently-proposed algorithm for
construction of probabilistic classifiers. This paper presents an empirical
comparison of the MBBC algorithm with three other Bayesian classifiers: Naive
Bayes, Tree-Augmented Naive Bayes and a general Bayesian network. All of these
are implemented using the K2 framework of Cooper and Herskovits. The
classifiers are compared in terms of their performance (using simple accuracy
measures and ROC curves) and speed, on a range of standard benchmark data sets.
It is concluded that MBBC is competitive in terms of speed and accuracy with
the other algorithms considered.Comment: 9 pages: Technical Report No. NUIG-IT-011002, Department of
Information Technology, National University of Ireland, Galway (2002
Simpler is better: a novel genetic algorithm to induce compact multi-label chain classifiers
Multi-label classification (MLC) is the task of assigning multiple class labels to an object based on the features that describe the object. One of the most effective MLC methods is known as Classifier Chains (CC). This approach consists in training q binary classifiers linked in a chain, y1 → y2 → ... → yq, with each responsible for classifying a specific label in {l1, l2, ..., lq}. The chaining mechanism allows each individual classifier to incorporate the predictions of the previous ones as additional information at classification time. Thus, possible correlations among labels can be automatically exploited. Nevertheless, CC suffers from two important drawbacks: (i) the label ordering is decided at random, although it usually has a strong effect on predictive accuracy; (ii) all labels are inserted into the chain, although some of them might carry irrelevant information to discriminate the others. In this paper we tackle both problems at once, by proposing a novel genetic algorithm capable of searching for a single optimized label ordering, while at the same time taking into consideration the utilization of partial chains. Experiments on benchmark datasets demonstrate that our approach is able to produce models that are both simpler and more accurate
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