Abstract
In this paper we propose a new method to perform incremental discretization. This approach consists in splitting the task in two layers. The first layer receives the sequence of input data and stores statistics of this data, using a higher number of intervals than what is usually required. The final discretization is generated by the second layer, based on the statistics stored by the previous layer. The proposed architecture processes streaming examples in a single scan, in constant time and space even for infinite sequences of examples. We demonstrate with examples that incremental discretization achieves better results than batch discretization, maintaining the performance of learning algorithms. The proposed method is much more appropriate to evaluate incremental algorithms, and in problems where data flows continuously as most of recent data mining applications.

Abstract

Abstract
The purpose of this paper is to describe an adaptive algorithm for improving the performance of Bayesian Network Classifiers (BNCs) in an on-line learning framework. Instead of choosing a priori a particular model class of BNCs, our adaptive algorithm scales up the model's complexity by gradually increasing the number of allowable dependencies among features, Starting with the simple Naive Bayes structure, it uses simple decision rules based on qualitative information about the performance's dynamics to decide when it makes sense to do the next move in the spectrum of feature dependencies and to start searching for a more complex classifier. Results in conducted experiments using the class of Dependence Bayesian Classifiers on three large datasets show that our algorithm is able to select a model with the appropriate complexity for the current amount of training data, thus balancing the computational cost of updating a model with the benefits of increasing in accuracy.

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