Most of the previous research on term weighting for information retrieval has focused on developing specialized parametric term weighting functions. Examples include TF.IDF vector-space formulations, BM25, and language modeling weighting. Each of these term weighting functions takes on a specific parametric form. While these weighting functions have proven to be highly effective, they impose st...

Bags of visual words are the most studied image description technique in the last years. This representation of images raised new possibilities as well as new research issues. In particular, it is important to automatically determine which visual words are the most relevant to describe the images, and which ones should be ignored. This issue is a classical problem of textual information retriev...

Image interpolation algorithms pervade many modern image processing and analysis applications. However, when their weighting schemes inefficiently generate very unrealistic estimates, they may negatively affect the performance of end-user Therefore, in this work, author introduced four based on some geometric shapes for digital operations. Moreover, quantity used to express extent each shape’s ...

This paper presents term-weighting schemes that have been evolved using genetic programming in an adhoc Information Retrieval model. We create an entire term-weighting scheme by firstly assuming that term-weighting schemes contain a global part, a term-frequency influence part and a normalisation part. By separating the problem into three distinct phases we reduce the search space and ease the ...

Term weighting schemes often dominate the performance of many classifiers, such as kNN, centroid-based classifier and SVMs. The widely used term weighting scheme in text categorization, i.e., tf.idf, is originated from information retrieval (IR) field. The intuition behind idf for text categorization seems less reasonable than IR. In this paper, we introduce inverse category frequency (icf) int...

Term weighting schemes often dominate the performance of many classifiers, such as kNN, centroid-based classifier and SVMs. The widely used term weighting scheme in text categorization, i.e., tf.idf, is originated from information retrieval (IR) field. The intuition behind idf for text categorization seems less reasonable than IR. In this paper, we introduce inverse category frequency (icf) int...

Clustering ensembles has been recently recognized as an emerging approach to provide more robust solutions to the data clustering problem. Current methods of clustering ensembles typically fall into instance-based, cluster-based, or hybrid approaches; however, most of such methods fail in discriminating among the various clusterings that participate to the ensemble. In this paper, we address th...

In order to achieve relevant scholarly information from the biomedical databases, researchers generally use technical terms as queries such as proteins, genes, diseases, and other biomedical descriptors. However, the technical terms have limits as query terms because there are so many indirect and conceptual expressions denoting them in scientific literatures. Combinatorial weighting schemes ar...

This paper presents two new ways of example weighting for subgroup discovery. The proposed example weighting schemes are applicable to any subgroup discovery algorithm that uses the weighted covering approach to discover interesting subgroups in data. To show the implications that the new example weighting schemes have on subgroup discovery, they were implemented in the APRIORI-SD algorithm. RO...