We consider the problem of detecting rooftops in overhead images, which is one processing step in a building detection system. Currently, the system uses a hand-configured linear classifier to select the most promising rooftop candidates for further processing. We present results from an empirical study in which we used machine learning methods to acquire the selection criteria for rooftops. RO...

This paper presents a new hybrid classifier that combines the probability based Bayesian Network paradigm with the Nearest Neighbor distance based algorithm. The Bayesian Network structure is obtained from the data by using the K2 structural learning algorithm. The Nearest Neighbor algorithm is used in combination with the Bayesian Network in the deduction phase. For those data bases in which s...

This research proposes a new approach to improve information retrieval systems based on multinomial naive Bayes classifier (MNBC), Bayesian networks (BNs), and multi-terminology which includes MeSH thesaurus (Medical Subject Headings) SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms). Our approach, is entitled improving semantic (IMSIR), extracts disambiguates concepts retrieves ...

Bayesian network are powerful probabilistic graphical models for modelling uncertainty. Among others, classification represents an important application: some of the most used classifiers are based on Bayesian networks. Bayesian networks are precise models: exact numeric values should be provided for quantification. This requirement is sometimes too narrow. Sets instead of single distributions ...

We present a framework for characterizing Bayesian classification methods. This framework can be thought of as a spectrum of allowable dependence in a given probabilistic model with the Naive Bayes algorithm at the most restrictive end and the learning of full Bayesian networks at the most general extreme. While much work has been carried out along the two ends of this spectrum, there has been ...

This work addresses the problem of having to train a Naïve Bayesian classifier using limited data. It first presents an improved instance-weighting algorithm that is accurate and robust to noise and then it shows how to combine it with a fine tuning algorithm to achieve even better classification accuracy. Our empirical work using 49 benchmark data sets shows that the improved instance-weightin...

This paper improves the naïve bayesian classification algorithm , combining with the rough set theory we can get a naive bayesian classifier algorithm based on the rough set. We implement this algorithm on a cloud platform using map-reduce programming mode and get a excellent result. A recall rate of 76.4 was achieved when classifying Tibetan Web pages .