Abstract
With the explosion of computer science in the last decade, data banks and networksmanagement present a huge part of tomorrows problems. One of them is the development of the best classication method possible in order to exploit the data bases. In classication problems, a representative successful method of the probabilistic model is a Naïve Bayes classier. However, the Naïve Bayes effectiveness still needs to be upgraded. Indeed, Naïve Bayes ignores misclassied instances instead of using it to become an adaptive algorithm. Different works have presented solutions on using Boosting to improve the Gaussian Naïve Bayes algorithm by combining Naïve Bayes classier and Adaboost methods. But despite these works, the Boosted Gaussian Naïve Bayes algorithm is still neglected in the resolution of classication problems. One of the reasons could be the complexity of the implementation of the algorithm compared to a standard Gaussian Naïve Bayes. We present in this paper, one approach of a suitable solution with a pseudo-algorithm that uses Boosting and Gaussian Naïve Bayes principles having the lowest possible complexity. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Highlights
In machine learning and statistics, classication is one of the most important tools to analyze and classify a large amount of data
The family of Naïve Bayes classier is commonly used as a probabilistic learning algorithm, by using the probability that a new observation belongs to a specic class
One technique to deal with continuous data is the Gaussian Naïve Bayes that assumes the continuous values associated with each class are distributed according to a Gaussian distribution parameterized by the corresponding means and standard derivations
Summary
In machine learning and statistics, classication is one of the most important tools to analyze and classify a large amount of data. Classication is the problem of identifying to which from several categories, a new observation belongs. The family of Naïve Bayes classier is commonly used as a probabilistic learning algorithm, by using the probability that a new observation belongs to a specic class. One technique to deal with continuous data is the Gaussian Naïve Bayes that assumes the continuous values associated with each class are distributed according to a Gaussian distribution parameterized by the corresponding means and standard derivations. It computes the posterior probability density function using normal distribution of classes.
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