To alleviate the problem of high dimensions in text clustering, an alternative to conventional methods is bipartite partitioning, where terms and documents are modeled as vertices on two sides respectively. Term weighting schemes, which assign weights to the edges linking terms and documents, are vital for the final clustering performance. In this paper, we conducted an comprehensive evaluation...

Text categorization is a task of automatically assigning documents to a set of predefined categories. Usually it involves a document representation method and term weighting scheme. This paper proposes a new term weighting scheme called Modified Inverse Document Frequency (MIDF) to improve the performance of text categorization. The document represented in MIDF is trained using the support vect...

the goal in information retrieval is to enable users to automatically and accurately retrieve data relevant to their queries. One possible approach to this problem is to use the vector space model, which models documents and queries as vectors in the term space. The components of the vectors are determined by the term weighting scheme. This paper compared between a selected set from the availab...

a r t i c l e i n f o a b s t r a c t The method based on Bag-of-visual-Words (BoW) deriving from local keypoints has recently appeared promising for video annotation. Visual word weighting scheme has critical impact to the performance of BoW method. In this paper, we propose a new visual word weighting scheme which is referred as emerging patterns weighting (EP-weighting). The EP-weighting sch...

We provide a comprehensive analysis of the out-of-sample predictive accuracy of different global vector autoregressive (GVAR) specifications based on alternative weighting schemes to address global spillovers across countries. In addition to weights based on bilateral trade, we entertain schemes based on different financial variables and geodesic distance. Our results indicate that models based...

This paper addresses the task of choosing prior weights for models that are to be used for weighted model averaging. Models that are very similar to each other should usually be given smaller weights than models that are quite distinct. Otherwise, the importance of a model in the weighted average could be increased by augmenting the set of models with duplicates of the model or virtual duplicat...

We introduce a weighting scheme for Voronoi diagrams that has preferred directions. This generalizes the concept of weighted Delaunay triangulations and overcomes some of the difficulties of using multiplicative anisotropic weight systems. We discuss properties that make these weighting schemes attractive.

We provide a simple but novel supervised weighting scheme for adjusting term frequency in tf-idf for sentiment analysis and text classification. We compare our method to baseline weighting schemes and find that it outperforms them on multiple benchmarks. The method is robust and works well on both snippets and longer documents.

While Distance Weighted Discrimination (DWD) is an appealing approach to classification in high dimensions, it was designed for balanced datasets. In the case of unequal costs, biased sampling, or unbalanced data, there are major improvements available, using appropriately weighted versions of DWD (wDWD). A major contribution of this paper is the development of optimal weighting schemes for var...

Recently the research on supervised term weighting has attracted growing attention in the field of Traditional Text Categorization (TTC) and Sentiment Analysis (SA). Despite their impressive achievements, we show that existing methods more or less suffer from the problem of over-weighting. Overlooked by prior studies, over-weighting is a new concept proposed in this paper. To address this probl...