Appraisal of companies is an important business activity. We mainly apply Bayesian networks for this classification task for Japanese electric company data. Firstly, few standard statistical techniques are performed. Then Bayesian networks are applied in four steps; (1) for implementing a current procedure of economical experts, where economical variables are clustered and then summarised for c...

In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that carries out a greedy search through the space of features. We hypothesize that this approach will improve asymptotic accuracy in domains that i...

The attribute conditional independence assumption of naive Bayes essentially ignores attribute dependencies and is often violated. On the other hand, although a Bayesian network can represent arbitrary attribute dependencies, learning an optimal Bayesian network classifier from data is intractable. Thus, learning improved naive Bayes has attracted much attention from researchers and presented m...

We investigate the performance of two machine learning algorithms in the context of antispam filtering. The increasing volume of unsolicited bulk e-mail (spam) has generated a need for reliable anti-spam filters. Filters of this type have so far been based mostly on keyword patterns that are constructed by hand and perform poorly. The Naive Bayesian classifier has recently been suggested as an ...

Data mining is a computational approach aiming to discover hidden and valuable information in large datasets. It has gained importance recently in the wide area of computational among which many in the domain of Business Informatics. This paper focuses on applications of data mining in Customer Relationship Management (CRM). The core of our application is a classifier based on the naive Bayesia...

The“curse of dimensionality”provides a powerful impetus to explore alternative data structures and representations for text processing. This paper presents a method for preparing a dataset for classification by determining the utility of a very small number of related dimensions via a Discriminative Multinomial Naive Bayes process, then using these utility measurements to weight these dimension...

It has recently been argued that a Naive Bayesian classifier can be used to filter unsolicited bulk e-mail (“spam”). We conduct a thorough evaluation of this proposal on a corpus that we make publicly available, contributing towards standard benchmarks. At the same time we investigate the effect of attribute-set size, training-corpus size, lemmatization, and stop-lists on the filter’s performan...

This study aimed to assess how specific components of an action could be selected by a simple computational system. We performed an experiment to test associations between grasps (precision or power grip) and several objects. We then ran simulations using a naive bayes classifier to study to what extent it could reproduce participants’ choice. This classifier had two learning matrices containin...

We present a method of handwritten numeral recognition, where we introduce hierarchical Gabor features (HGFs) and construct a Bayesian network classifier that encodes the dependence between HGFs. We extract HGFs in such a way that they represent different levels of information which are structured such that the lower the level is, the more localized information they have. At each level, we choo...