Open AccessParameter estimation for high dimensional classification model on colon cancer microarray dataset
Author(s) -
Bahriddin Abapihi,
M Mukhsar,
Gusti Ngurah Adhi Wibawa,
B Baharuddin,
Favorisen Rosyking Lumbanraja,
Mohammad Reza Faisal,
Asrul Sani
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1899/1/012113
Subject(s) - logistic regression , logistic model tree , statistics , estimation theory , dimension (graph theory) , mathematics , pattern recognition (psychology) , computer science , estimation , entropy (arrow of time) , artificial intelligence , data mining , engineering , physics , systems engineering , quantum mechanics , pure mathematics
In classification problems, logistic regression is among powerful techniques for discrimination. It provides directive probabilities of sample classification and interpretable coefficients. When it comes to model high dimensional dataset, however, logistic regression with its Newton-Raphson method of parameter estimation is no longer applicable, especially on low sample size and extremely high dimension. By applying cross-entropy algorithm on regularized logistic regression, it was able to well performing parameter estimation and highly accurate classification result.