A novel solution of the fall detection problem, based on the use of infrared depth sensors, is proposed. A methodology for acquisition of real-world data and their preprocessing is presented. The procedures for feature generation, preprocessing and selection are described. The naïve Bayes classifier is designed for the selected features and its performance is evaluated using a data set consisti...

This paper describes an approach to text classification using language models. This approach is a natural extension of the traditional Naïve Bayes classifier, in which we replace the Laplace smoothing by some more sophisticated smoothing methods. In this paper, we tested four smoothing methods commonly used in information retrieval. Our experimental results show that using a language model, we ...

Methods for extracting quantitative information regarding nuclear morphology from histopathology images have been long used to aid pathologists in determining the degree of differentiation in numerous malignancies. Most methods currently in use, however, employ the naïve Bayes approach to classify a set of nuclear measurements extracted from one patient. Hence, the statistical dependency betwee...

This paper intends to classify the Ljubljana Breast Cancer dataset using C4.5 Decision Tree and Naïve Bayes classifiers. In this work, classification is carriedout using two methods. In the first method, dataset is analysed using all the attributes in the dataset. In the second method, attributes are ranked using information gain ranking technique and only the high ranked attributes are used to...

In this study, we focus on the problem of spam detection. Based on a cellular automaton approach and naïve Bayes technique which are built as individual classifiers we evaluate a novel method combining multiple classifiers diversified both by feature selection and different classifiers to determine whether we can more accurately detect Spam. This approach combines decisions from three cellular ...

In this paper, we compare the performance of a variety of machine learning algorithms, including supervised Naïve Bayes, J48, SVM, Random Tree, Random Forest, and non-supervised KNN for determining the type of cancer a patient is su ering using medical textual records. We train these classi ers on di erent sets of features such as unigrams and bigrams of words, character n-grams using tf-idf we...

In this paper we present an approach for estimating the quality of machine translation system. There are various methods for estimating the quality of output sentences, but in this paper we focus on Naïve Bayes classifier to build model using features which are extracted from the input sentences. These features are used for finding the likelihood of each of the sentences of the training data wh...

The Markov Blanket Bayesian Classifier is a recentlyproposed algorithm for construction of probabilistic classifiers. This paper presents an empirical comparison of the MBBC algorithm with three other Bayesian classifiers: Naïve Bayes, Tree-Augmented Naïve Bayes and a general Bayesian network. All of these are implemented using the K2 framework of Cooper and Herskovits. The classifiers are comp...

This paper introduces a new Bayesian network structure, named a Partial Bayesian Network (PBN), and describes an algorithm for constructing it. The PBN is designed to be used for classification tasks, and accordingly the algorithm constructs an approximate Markov blanket around a classification node. Initial experiments have compared the performance of the PBN algorithm with Naïve Bayes, Tree-A...