This paper presents the development of a Bayesian belief network classifier for prediction of graft status and survival period in renal transplantation using the patient profile information prior to the transplantation. The objective was to explore feasibility of developing a decision making tool for identifying the most suitable recipient among the candidate pool members. The dataset was compi...

We investigate algebraic, logical, and geometric properties of concepts recognized by various classes of probabilistic classifiers. For this we introduce a natural hierarchy of probabilistic classifiers, the lowest level of which comprises the naive Bayesian classifiers. We show that the expressivity of classifiers on the different levels in the hierarchy is characterized algebraically by separ...

In this paper, we propose a generic description of the concept lattice as classifier in an iterative recognition process. The experimentation is realized on the noised symbols of GREC database [6]. Our experimentation presents a comparison with the two classical numerical classifiers that are the bayesian classifier and the nearest neighbors classifier and some comparison elements for an iterat...

Understanding proteins functions is a major goal in the post-genomic era. Proteins usually work in context of other proteins and rarely function alone. Therefore, it is highly relevant to study the interaction partners of a protein in order to understand its function. Machine learning techniques have been widely applied to predict protein-protein interactions. Kernel functions play an important...

This package installs and interfaces the naive Bayesian classifier for 16S rRNA sequences developed by the Ribosomal Database Project (RDP). With this package the classifier trained with the standard training set can be used or a custom classifier can be trained.

As one of the most common types of graphical models, the Bayesian classifier has become an extremely popular approach to dealing with uncertainty and complexity. The scoring functions once proposed and widely used for a Bayesian network are not appropriate for a Bayesian classifier, in which class variable C is considered as a distinguished one. In this paper, we aim to clarify the working mech...

It is known that Naive Bayesian classifier (NB) works very well on some domains, and poorly on some. The performance of NB suffers in domains that involve correlated features. C4.5 decision trees, on the other hand, typically perform better than the Nafve Bayesian algorithm on such domains. This paper describes a Selective Bayesian classifier (SBC) that simply uses only those features that C4.5...

Classifier combination methods need to make best use of the outputs of multiple, imperfect classifiers to enable higher accuracy classifications. In many situations, such as when human decisions need to be combined, the base decisions can vary enormously in reliability. A Bayesian approach to such uncertain combination allows us to infer the differences in performance between individuals and to...

This paper proposes a two-step method for Chinese text categorization (TC). In the first step, a Naïve Bayesian classifier is used to fix the fuzzy area between two categories, and, in the second step, the classifier with more subtle and powerful features is used to deal with documents in the fuzzy area, which are thought of being unreliable in the first step. The preliminary experiment validat...

In this work, we make a contribution to natural speech dialogue act detection. We focus our attention on the dialogue act classification using a Bayesian approach. Our classifier is tested on two corpora, the Switchboard and the Basurde tasks. A combination of a naive Bayes classifier and n-grams is used. The impact of different smoothing methods (Laplace and Witten Bell) and n-grams in classif...