Low-Precision Feature Selection on Microarray Data: An Information Theoretic Approach
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Low-Precision Feature Selection on Microarray Data: An Information Theoretic ApproachData
2022Cita bibliográfica
Morán-Fernández, L., Bolón-Canedo, V. & Alonso-Betanzos, A. Low-precision feature selection on microarray data: an information theoretic approach. Med Biol Eng Comput 60, 1333–1345 (2022). https://doi.org/10.1007/s11517-022-02508-0
Resumo
[Abstract] The number of interconnected devices, such as personal wearables, cars, and smart-homes, surrounding us every day has recently increased. The Internet of Things devices monitor many processes, and have the capacity of using machine learning models for pattern recognition, and even making decisions, with the added advantage of diminishing network congestion by allowing computations near to the data sources. The main restriction is the low computation capacity of these devices. Thus, machine learning algorithms capable of maintaining accuracy while using mechanisms that exploit certain characteristics, such as low-precision versions, are needed. In this paper, low-precision mutual information-based feature selection algorithms are employed over DNA microarray datasets, showing that 16-bit and some times even 8-bit representations of these algorithms can be used without significant variations in the final classification results achieved.
Palabras chave
Microarray data
Low precision
Feature selection
Mutual information
Classification
Edge computing
Internet of Things
Low precision
Feature selection
Mutual information
Classification
Edge computing
Internet of Things
Descrición
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG
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Atribución 4.0 internacional