Development of partial least squares regression with discriminant analysis for software bug prediction [PDF]
HeliyonMany prediction models and approaches have been introduced during the past decades that try to forecast bugged code elements based on static source code metrics, change and history metrics, or both.
Róbert Rajkó +3 more
doaj +2 more sources
Partial Least Squares Regression-Based Robust Forward Control of the Tableting Process [PDF]
Pharmaceutics, 2020 In this study, we established a robust feed-forward control model for the tableting process by partial least squares regression using the near-infrared (NIR) spectra and physical attributes of the granules to be compressed.
Yusuke Hattori +2 more
doaj +2 more sources
Gene Function Prediction from Functional Association Networks Using Kernel Partial Least Squares Regression. [PDF]
PLoS ONE, 2015 With the growing availability of large-scale biological datasets, automated methods of extracting functionally meaningful information from this data are becoming increasingly important.
Sonja Lehtinen +4 more
doaj +3 more sources
Tide modeling using partial least squares regression [PDF]
Ocean Dynamics, 2020 This research explores the novel use of the partial least squares regression (PLSR) as an alternative model to the conventional least squares (LS) model for modeling tide levels. The modeling is based on twenty tidal constituents: M2, S2, N2, K1, O1, MO3, MK3, MN4, M4, SN4, MS4, 2MN6, M6, 2MS6, S4, SK3, 2MK5, 2SM6, 3MK7, and M8.
Onuwa, Okwuashi +2 more
openaire +2 more sources
Marginal Screening for Partial Least Squares Regression
IEEE Access, 2017 Partial least squares (PLS) regression is a versatile modeling approach for high-dimensional data analysis. Recently, PLS-based variable selection has attracted great attention due to high-throughput data reduction and modeling interpretability.
Naifei Zhao, Qingsong Xu, Hong Wang
doaj +2 more sources
Fitting and Cross-Validating Cox Models to Censored Big Data With Missing Values Using Extensions of Partial Least Squares Regression Models [PDF]
Frontiers in Big Data, 2021 Fitting Cox models in a big data context -on a massive scale in terms of volume, intensity, and complexity exceeding the capacity of usual analytic tools-is often challenging. If some data are missing, it is even more difficult.
Frédéric Bertrand +3 more
doaj +2 more sources
Partial Least Squares Regression
Citation: 'partial least squares regression' in the IUPAC Compendium of Chemical Terminology, 5th ed.; International Union of Pure and Applied Chemistry; 2025. Online version 5.0.0, 2025. 10.1351/goldbook.10155 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual terms ...
R. Dennis Cook, Liliana Forzani
semanticscholar +3 more sources
A fresh-cut papaya freshness prediction model based on partial least squares regression and support vector machine regression [PDF]
HeliyonThis study investigated the physicochemical and flavor quality changes in fresh-cut papaya that was stored at 4 °C. Multivariate statistical analysis was used to evaluate the freshness of fresh-cut papaya.
Liyan Rong +8 more
doaj +2 more sources
HB-PLS: A statistical method for identifying biological process or pathway regulators by integrating Huber loss and Berhu penalty with partial least squares regression [PDF]
Forestry Research, 2021 Gene expression data features high dimensionality, multicollinearity, and non-Gaussian distribution noise, posing hurdles for identification of true regulatory genes controlling a biological process or pathway. In this study, we integrated the Huber loss
Wenping Deng +4 more
doaj +2 more sources
Partial Least Squares Regression for Binary Data
MathematicsClassical Partial Least Squares Regression (PLSR) models were developed primarily for continuous data, allowing dimensionality reduction while preserving relationships between predictors and responses. However, their application to binary data is limited.
Laura Vicente-Gonzalez +2 more
doaj +2 more sources