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

## TL;DR

This paper introduces extensions of PLSGLR combined with logistic regression and LDA for microarray data classification, compares them with classical methods, and demonstrates that KMA achieves the lowest error rates.

## Contribution

It develops new PLSGLR-based classifiers and compares their performance with existing methods, highlighting KMA's superior accuracy on microarray data.

## Key findings

- KMA has the lowest classification error rates among tested methods.
- Extensions of PLSGLR improve classification performance.
- KMA outperforms classical classifiers on both preprocessed and raw data.

## Abstract

We implement extensions of the partial least squares generalized linear regression (PLSGLR) due to Bastien et al. (2005) through its combination 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). These two classification methods are then compared with classical methodologies like the k-nearest neighbours (KNN), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), ridge partial least squares (RPLS), and support vector machines(SVM). Furthermore, we implement the kernel multilogit algorithm (KMA) by Dalmau et al. (2015)and compare its performance with that of the other classifiers. The results indicate that for both un-preprocessed and preprocessed data, the KMA has the lowest classification error rates.