Omar Fetitah¹, Ibrahim M. Almanjahie^2,3, Mohammed Kadi Attouch^1,*, Salah Khardani⁴

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2681-2694, 2021, DOI:10.32604/cmc.2021.015469 - 05 February 2021

Abstract The problem of predicting continuous scalar outcomes from functional predictors has received high levels of interest in recent years in many fields, especially in the food industry. The k-nearest neighbor (k-NN) method of Near-Infrared Reflectance (NIR) analysis is practical, relatively easy to implement, and becoming one of the most popular methods for conducting food quality based on NIR data. The k-NN is often named k nearest neighbor classifier when it is used for classifying categorical variables, while it is called k-nearest neighbor regression when it is applied for predicting noncategorical variables. The objective of this paper is to… More >

Mir Subzar¹, Amer Ibrahim Al-Omari², Ayed R. A. Alanzi^{3, *}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 125-138, 2020, DOI:10.32604/cmc.2020.010230 - 23 July 2020

Abstract The ordinary least square (OLS) method is commonly used in regression analysis. But in the presence of outlier in the data, its results are unreliable. Hence, the robust regression methods have been suggested for a long time as alternatives to the OLS to solve the outliers problem. In the present study, new ratio type estimators of finite population mean are suggested using simple random sampling without replacement (SRSWOR) utilizing the supplementary information in Bowley’s coefficient of skewness with quartiles. For these proposed estimators, we have used the OLS, Huber-M, Mallows GM-estimate, Schweppe GM-estimate, and SIS More >

Liming Yang^1,2, Junjian Bai¹, Qun Sun³

CMES-Computer Modeling in Engineering & Sciences, Vol.108, No.1, pp. 49-65, 2015, DOI:10.3970/cmes.2015.108.049

Abstract Extreme learning machine (ELM) has demonstrated great potential in machine learning and data mining fields owing to its simplicity, rapidity and good generalization performance. In this work, a general framework for ELM regression is first investigated based on least absolute deviation (LAD) estimation (called LADELM), and then we develop two regularized LADELM formulations with the l₂-norm and l₁-norm regularization, respectively. Moreover, the proposed models are posed as simple linear programming or quadratic programming problems. Furthermore, the proposed models are used directly to analyze the hard rate of licorice seeds using near-infrared spectroscopy data. Experimental results on More >