In detail, this study aims specifically to the following objectives: (i) reduce the dimensionality of synset-based datasets, minimizing the information loss, i.e. lossless, (ii) check the performance of Naïve Bayes classifiers, both on the original datasets and on their combination with our SDRS lossless dimensional ...
This study addresses the problem of feature reduction by introducing a new semantic-based proposal (SDRS) that avoids losing knowledge (lossless). Synset-features can be semantically grouped by taking advantage of taxonomic relations ...
This study has provided an experimental comparison of the performance that can be achieved by using token and semantic based representations of texts for spam filtering purposes. Moreover, we have introduced and shown the performance of a feature reduction scheme based on an MOEA optimization scheme (SDRS).
The contribution of this study is to introduce different dimensionality reduction methods (lossless, low-loss and lossy) as an optimization problem that can ...
SDRS: A new lossless dimensionality reduction for text corpora. https://doi.org/10.1016/j.ipm.2020.102249 ·. Journal: Information Processing & Management, 2020, № 4, p. 102249. Publisher: Elsevier BV. Authors: Iñaki Velez de Mendizabal, Vitor Basto-Fernandes, Enaitz Ezpeleta, José R. Méndez, Urko Zurutuza. Funders.
SDRS: A new lossless dimensionality reduction for text corpora · 2020 Volume 57 Issue 4 · ipm_journal · https://doi.org/10.1016/j.ipm.2020.102249 · 10.1016/j.ipm.
SDRS: A new lossless dimensionality reduction for text corpora ; dc.subject · dc.subject ; Semantic-based feature reduction · Multi-objective evolutionary ...
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Our algorithm is similar to the method of “association analysis” in statistics, though the feature extraction context as well as the information theoretic and geometric interpretation are new. The algorithm is illustrated by various synthetic co-occurrence data. It is then demonstrated for text categorization and ...
Feb 8, 2023 · SDRS dimensionality reduction method uses multi-objective evolutionary algorithms. (MOEA) to identify the maximum level to which each synset can ... new lossless dimensionality reduction for text corpora. Information Processing and Management. 57(4):102249 DOI 10.1016/j.ipm.2020.102249. Verma S ...
Abstract. Dimensionality reduction of empirical co-occurrence data is a fundamental problem in unsuper- vised learning. It is also a well studied problem in statistics known as the analysis of cross-classified data. One principled approach to this problem is to represent the data in low dimension with min-.