Search results for "Bayes classifier"
showing 10 items of 33 documents
Defining classifier regions for WSD ensembles using word space features
2006
Based on recent evaluation of word sense disambiguation (WSD) systems [10], disambiguation methods have reached a standstill. In [10] we showed that it is possible to predict the best system for target word using word features and that using this 'optimal ensembling method' more accurate WSD ensembles can be built (3-5% over Senseval state of the art systems with the same amount of possible potential remaining). In the interest of developing if more accurate ensembles, w e here define the strong regions for three popular and effective classifiers used for WSD task (Naive Bayes – NB, Support Vector Machine – SVM, Decision Rules – D) using word features (word grain, amount of positive and neg…
Diagnóstico de Enfermedades Card´ıacas con los algoritmos supervisados Naives Bayesian
2020
Las enfermedades cardíacas son la principal causa de muerte en la actualidad. Este paper contrasta la performance de los diferentes algoritmos supervisados de Machine Learning, que tienen aplicaciones en el a´rea de la medicina, con los algoritmos supervisados Naives Bayes para ayudar a clasificar pacientes propensos a sufrir enfermedades cardíacas. Como fuente de datos se usan 303 instancias de pacientes con diferentes características que fueron analizados al procesar los datos con los respectivos algoritmos. Los resultados con el algoritmo de Naives Bayes son pro- metedores, obteniendo una precisio´n del 86,81 %, usando la fuente de datos mencionada. Esta familia de algoritmos tiene un me…
Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers
2016
In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called the Online Histogram-based Naïve Bayes Classifier (OHNBC) involves a statistical classifier based on the well-established Bayesian theory, but which makes some assumptions with respect to the independence of the attributes. Moreover, this classifier generates a prediction model using uni-dimensional histograms, whose segments or buckets are fixed in terms of their cardinalities but dynamic in terms of their widths. Additionally, our algorithm invokes the principles of info…
Methodological Approach for Messages Classification on Twitter Within E-Government Area
2018
The constant growth in the numbers of Social Media users is a reality of the past few years. Companies, governments and researchers focus on extracting useful data from Social Media. One of the most important things we can extract from the messages transmitted from one user to another is the sentiment—positive, negative or neutral—regarding the subject of the conversation. There are many studies on how to classify these messages, but all of them need a huge amount of data already classified for training, data not available for Romanian language texts. We present a case study in which we use a Naive Bayes classifier trained on an English short text corpus on several thousand Romanian texts. …
Privacy Violation Classification of Snort Ruleset
2010
Published version of a paper presented at the 2010 18th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). (c) 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Paper also available from the publisher:http://dx.doi.org/10.1109/PDP.2010.87 It is important to analyse the privacy impact of Intrusion Detection System (IDS) rules, in order to understand a…
A Comparative Study to Analyze the Performance of Advanced Pattern Recognition Algorithms for Multi-Class Classification
2021
This study aims to implement the following four advanced pattern recognition algorithms, such as “optimal Bayesian classifier,” “anti-Bayesian classifier,” “decision trees (DTs),” and “dependence trees (DepTs)” on both artificial and real datasets for multi-class classification. Then, we calculated the performance of individual algorithms on both real and artificial data for comparison. In Sect. 1, a brief introduction is given about the study. In the second section, the different types of datasets used in this study are discussed. In the third section, we compared the classification accuracies of Bayesian and anti-Bayesian methods for both the artificial and real-life datasets. In the four…
Classification of Targets Using Statistical Features from Range FFT of mmWave FMCW Radars
2021
Radars with mmWave frequency modulated continuous wave (FMCW) technology accurately estimate the range and velocity of targets in their field of view (FoV). The targeted angle of arrival (AoA) estimation can be improved by increasing receiving antennas or by using multiple-input multiple-output (MIMO). However, obtaining target features such as target type remains challenging. In this paper, we present a novel target classification method based on machine learning and features extracted from a range fast Fourier transform (FFT) profile by using mmWave FMCW radars operating in the frequency range of 77–81 GHz. The measurements are carried out in a variety of realistic situations, including p…
Statistical classification and proportion estimation - an application to a macroinvertebrate image database
2010
We apply and compare a random Bayes forest classifier and three traditional classification methods to a dataset of complex benthic macroinvertebrate images of known taxonomical identity. Since in biomonitoring changes in benthic macroinvertebrate taxa proportions correspond to changes in water quality, their correct estimation is pivotal. As classification errors are passed on to the allocated proportions, we explore a correction method known as a confusion matrix correction. Classification methods were compared using the misclassification error and the χ2 distance measures of the true proportions to the allocated and to the corrected proportions. Using low misclassification error and small…
Breaking the curse of dimensionality in quadratic discriminant analysis models with a novel variant of a Bayes classifier enhances automated taxa ide…
2013
Macroinvertebrate samples are commonly used in biomonitoring to study changes on aquatic ecosystems. Traditionally, specimens are identified manually to taxa by human experts being time-consuming and cost intensive. Using the image data of 35 taxa and 64 features, we propose a novel variant of the quadratic discriminant analysis for breaking the curse of dimensionality in quadratic discriminant analysis models. Our variant, called a random Bayes array (RBA), uses bagging and random feature selection similar to random forest. We explore several variations of RBA. We consider three classification (i.e taxa identification) decisions: majority vote, averaged posterior probabilities, and a novel…
A New Technique for Vibration-Based Diagnostics of Fatigued Structures Based on Damage Pattern Recognition via Minimization of Misclassification Prob…
2017
Vibration-based diagnostics provide various methods to detect, locate, and characterize damage in structural and mechanical systems by examining changes in measured vibration response. Research in vibration-based damage recognition has been rapidly expanding over the last few years. The basic idea behind this technology is that modal parameters (notably frequencies, mode shapes, and modal damping) are functions of the physical properties of the structure (mass, damping, and stiffness). Therefore, changes in the physical properties will cause detectable changes in the modal properties. In investigations, many techniques were applied to recognize damage in structural and mechanical systems, b…