ntroduction: Diagnosing brain tumor is not always easy for doctors, and existence of an assistant that                                                      facilitates the interpretation process is an asset in the clinic. Computer vision techniques are devised to aid the clinic in detecting tumors based on a database of tumor c...

Pre-processing the dataset is an important stage in the Knowledge Discovery in Datasets (KDD) process. Filtering noise through instance selection is a necessary task. With this, the risk to use misclassified and non-representative instances to train supervised classifiers is reduced. This study aims at improving the performance of the Gamma associative classifier, by introducing a novel similar...

Learning an appropriate (dis)similarity function from the available data is a central problem in machine learning, since the success of many machine learning algorithms critically depends on the choice of a similarity function to compare examples. Despite many approaches to similarity metric learning that have been proposed, there has been little theoretical study on the links between similarit...

The first aim of this paper is to extend the fuzzy similarity relation defined in the generalized Łukasiewicz structure to utilize common and cumulative Minkowsky metrics and introduce a new classifier based on the cumulative similarity measure which uses the Minkowsky metrics in the generalized Łukasiewicz structure. The second aim of this paper is to study properties of this classifier when a...

This paper addresses the question of how document classifiers can exploit implicit information about document similarity to improve document classifier accuracy. We infer document similarity using simple n-gram overlap, and demonstrate that this improves overall document classification performance over two datasets. As part of this, we find that collective classification based on simple iterati...

This paper addresses the question of how document classifiers can exploit implicit information about document similarity to improve document classifier accuracy. We infer document similarity using simple n-gram overlap, and demonstrate that this improves overall document classification performance over two datasets. As part of this, we find that collective classification based on simple iterati...

Machine learning algorithms have been successfully employed in solving the record linkage problem. Machine learning casts the record linkage problem as a classification problem by training a classifier that classifies 2 records as duplicates or unique. Irrespective of the machine learning algorithm used, the initial step in training a classifier involves selecting a set of similarity functions ...

We present a binary graph classifier (BGC) which allows to classify large, unweighted, undirected graphs. This classifier is based on a local decomposition of the graph for each node in generalized trees. The obtained trees, forming the tree set of the graph, are then pairwise compared by a generalized treesimilarity-algorithm (GTSA) and the resulting similarity scores determine a characteristi...