Authors:
Jaime Salvador-Meneses
1
;
Zoila Ruiz-Chavez
1
and
Jose Garcia-Rodriguez
2
Affiliations:
1
Universidad Central del Ecuador, Ciudadela Universitaria, Quito and Ecuador
;
2
Universidad de Alicante, Ap. 99. 03080, Alicante and Spain
Keyword(s):
Big Data, Compression, Processing, Categorical Data, BLAS.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Data Reduction and Quality Assessment
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Pre-Processing and Post-Processing for Data Mining
;
Soft Computing
;
Symbolic Systems
Abstract:
The machine learning algorithms, prior to their application, require that the information be stored in memory. Reducing the amount of memory used for data representation clearly reduces the number of operations required to process it. Many of the current libraries represent the information in the traditional way, which forces you to iterate the whole set of data to obtain the desired result. In this paper we propose a technique to process categorical information previously encoded using the bit-level schema, the method proposes a block processing which reduces the number of iterations on the original data and, at the same time, maintains a processing performance similar to the processing of the original data. The method requires the information to be stored in memory, which allows you to optimize the volume of memory consumed for representation as well as the operations required to process it. The results of the experiments carried out show a slightly lower time processing than the o
btained with traditional implementations, which allows us to obtain a good performance.
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