The exponential increase in the volume of medical image database has imposed new challenges to clinical routine in maintaining patient history, diagnosis, treatment and monitoring. With the advent of data mining and machine learning techniques it is possible to automate and/or assist physicians in clinical diagnosis. In this research a medical image classification framework using data mining te...

This paper focuses on a bias variance decomposition analysis of a local learning algorithm, the nearest neighbor classiier, that has been extended with error correcting output codes. This extended algorithm often considerably reduces the 0-1 (i.e., classiication) error in comparison with nearest neighbor (Ricci & Aha, 1997). The analysis presented here reveals that this performance improvement ...

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In this paper, we propose a new mechanism for counteracting ARP (Address Resolution Protocol) poisoning-based Man-in-the-Middle (MITM) attacks in a subnet, where wired and wireless nodes can coexist. The key idea is that even a new node can be protected from an ARP cache poisoning attack if the mapping between an IP and the corresponding MAC addresses is resolved through fair voting among neigh...

The substitution of missing values, also called imputation, is an important data preparation task for data mining applications. This paper describes a nearest-neighbor method to impute missing values, showing that it can be useful for a clustering genetic algorithm. The proposed nearest-neighbor method is assessed by means of simulations performed in two datasets that are benchmarks for data mi...

The k-nearest-neighbor rule is a well known pattern recognition technique with very good results in a great variety of real classification tasks. Based on the neighborhood concept, several classification rules have been proposed to reduce the error rate of the k-nearest-neighbor rule (or its time requirements). In this work, two new geometrical neighborhoods are defined and the classification r...

This paper presents the results of the application of an instance-based learning algorithm k-Nearest Neighbor Method on Feature Projections (k-NNFP) to text categorization and compares it with k-Nearest Neighbor Classiier (k-NN). k-NNFP is similar to k-NN except it nds the nearest neighbors according to each feature separately. Then it combines these predictions using a majority voting. This pr...

Recently, a new pattern classiier using neighborhood information in the framework of the Dempster-Shafer theory of evidence was introduced 3, 2]. This approach consists in considering each neighbor of a pattern to be classiied as an item of evidence supporting certain hypotheses concerning the class membership of that pattern. In this paper, an adaptive version of this method is proposed, in wh...

Automatic image annotation is a process in which computer systems automatically assign the textual tags related with visual content to a query image. In most cases, inappropriate tags generated by the users as well as the images without any tags among the challenges available in this field have a negative effect on the query's result. In this paper, a new method is presented for automatic image...