We present an approach to textual classification based on the suffix tree data structure and apply it to spam filtering. A method for scoring of documents using the suffix tree is developed and a number of scoring and score normalisation functions are tested. Our results show that the character level representation of documents and classes facilitated by the suffix tree significantly improves c...

Naive Bayes induction algorithm is very popular in classification field. Traditional method for dealing with numeric data is to discrete numeric attributes data into symbols. The difference of distinct discredited criteria has significant effect on performance. Moreover, several researches had recently employed the normal distribution to handle numeric data, but using only one value to estimate...

Classification is an important data mining technique with broad applications. It classifies data of various kinds. Classification is used in every field of our life. Classification is used to classify each item in a set of data into one of predefined set of classes or groups. This paper has been carried out to make a performance evaluation of Naïve Bayes and j48 classification algorithm. Naive ...

It is well-known that naive Bayes performs surprisingly well in classification, but its probability estimation is poor. In many applications, however, a ranking based on class probabilities is desired. For example, a ranking of customers in terms of the likelihood that they buy one’s products is useful in direct marketing. What is the general performance of naive Bayes in ranking? In this paper...

This work describes a genetic algorithm for the calculation of substructural analysis for use in ligand-based virtual screening. The algorithm is simple in concept and effective in operation, with simulated virtual screening experiments using the MDDR and WOMBAT data sets showing it to be superior to substructural analysis weights based on a naive Bayesian classifier.

When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous vari­ ables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon the normality as­ sumption and instead use statistical methods for nonparametric density estimation. For a n...

Feature selection has proved to be an effective way to reduce the model complexity while giving a relatively desirable accuracy, especially, when data is scarce or the acquisition of some feature is expensive. However, the single selected model may not always generalize well for unseen test data whereas other models may perform better. Bayesian Model Averaging (BMA) is a widely used approach to...

We compare the performance of several machine learning algorithms in the problem of prognostics of the femoral neck fracture recovery: the K-nearest neighbours algorithm, the semi-naive Bayesian classifier, backpropagation with weight elimination learning of the multilayered neural networks, the LFC (lookahead feature construction) algorithm, and the Assistant-I and Assistant-R algorithms for t...

Metagenomics experiments often characterize microbial communities by sequencing the ribosomal 16S and ITS regions. Taxonomy prediction is a fundamental step in such studies. The SINTAX algorithm predicts taxonomy by using k-mer similarity to identify the top hit in a reference database and provides bootstrap confidence for all ranks in the prediction. SINTAX achieves comparable or better accura...