PLS Generalized Linear Regression and Kernel Multilogit Algorithm (KMA) for Microarray Data Classification Problem

• Autores: Adolphus Wagala, Graciela González Farías, Rogelio Ramos, Oscar Dalmau
• Localización: Revista Colombiana de Estadística, ISSN-e 2389-8976, ISSN 0120-1751, Vol. 43, Nº. 2, 2020, págs. 233-249
• Idioma: inglés
• DOI: 10.15446/rce.v43n2.81811
• Títulos paralelos:
  • Regresión lineal generalizada por MCP y algoritmo kernel multilogit para la clasificación de datos de microarreglos
• Enlaces
  • Texto completo
• Resumen
  • español

    Resumen Este estudio combina el modelo de regresión lineal generalizado por mínimos cuadrado parciales (RLGMCP), con regresión logística y análisis discriminante lineal, para obtener los modelos de regresión logística generalizada por mínimos cuadrados parciales, (RLGMCP) y regresión logística generalizada-discriminante por mínimos cuadrados parciales (RLGDMCP). Se realiza un estudio comparativo con clasificadores clásicos como, fc-vecinos más cercanos (KVC), análisis discriminante lineal (ADL), análisis discriminante de por mínimos cuadrados parciales (ADMCP), regresión por mínimos cuadrados parciales (RMCP) y máquinas de vectores de soporte de soporte vectorial (MSV). Además, se implementa una nueva metodología conocida como algoritmo de kernel multilogit (AKM). Su desempeño es comparado con los de los otros clasificadores. De acuerdo con las tasas de error de clasificación obtenidas a partir de los diferentes tipos de datos, el KMA es el de mejor resultado.

  • English

    Abstract This study involves the implentation of the extensions of the partial least squares generalized linear regression (PLSGLR) by combining it with logistic regression and linear discriminant analysis, to get a partial least squares generalized linear regression-logistic regression model (PLSGLR-log), and a partial least squares generalized linear regression-linear discriminant analysis model (PLSGLRDA). A comparative study of the obtained classifiers with the classical methodologies like the fc-nearest neighbours (KNN), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), ridge partial least squares (RPLS), and support vector machines(SVM) is then carried out. Furthermore, a new methodology known as kernel multilogit algorithm (KMA) is also implemented and its performance compared with those of the other classifiers. The KMA emerged as the best classifier based on the lowest classification error rates compared to the others when applied to the types of data are considered; the un-preprocessed and preprocessed.

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