In this study we deal with the mixing problem, which concerns combining the prediction of independently trained local models to a global prediction. We deal with it from the perspective of Learning Classifier Systems where a set of classifiers provide the local models. Firstly, we formalise

Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4.5. This fact raises the question of whether a classifier with less

In the modern era of computer and technology, images and videos play a vital role. Therefore, there is always a need for robust skin detection system in order to cope with the intolerable and objectionable contents. In this paper, an efficient method has been implemented for skin detection, which detects the skin in different images under different environmental conditions. We have used the two machine learning approaches i.e. Random Forests and Multilayer perceptron for skin detection. We have also then combined the results of these two approaches used. We have used total of 554 images in our experiments. General Terms Skin detection, recognition, tracking, images, and classification

The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains

In this paper we present anaverage-case analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noise-free Boolean attributes. We calculate the probability

The naive Bayes classifier, currently experiencing a renaissance in machine learning, has long been a core technique in information retrieval. We review some of the variations of naive Bayes models used for text retrieval and classification, focusing on the distributional assump- tions made

using user-independent classifiers of microcapillary electrophoresis traces

The simple Bayesian classifier (SBC) is commonly thought to assume that attributes are independent given the class, but this is apparently contradicted by the surprisingly good performance it exhibits in many domains that contain clear attribute dependences. No explanation for this has been

The combination of classifiers from independent observation domains has a myriad of benefits in practical pattern recognition problems. In this paper we propose a firm theoretical framework from which an upper bound on classifier combination performance can be calculated, based on mismatches

. Subjects were asked to perform a sequence of everyday tasks but not told specifically where or how to do them. Mean, energy, frequency-domain entropy, and correlation of acceleration data was calculated and several classifiers using these features were tested. Decision tree classifiers showed the best