RILS-ROLS: robust symbolic regression via iterated local search and ordinary least squares
نویسندگان
چکیده
Abstract In this paper, we solve the well-known symbolic regression problem that has been intensively studied and a wide range of applications. To it, propose an efficient metaheuristic-based approach, called RILS - ROLS . is based on following two elements: ( i ) iterated local search, which method backbone, mainly solving combinatorial some continuous aspects problem; ii ordinary least squares method, focuses aspect search space—it efficiently determines best—fitting coefficients linear combinations within solution equations. addition, introduce novel fitness function combines important model quality measures: $$R^2$$ R 2 score, RMSE size (or complexity), carefully designed allows systematic in proximity to candidate solution. Experiments are conducted ground-truth benchmark sets from literature: Feynman Strogatz was compared 14 other competitors literature. Our outperformed all with respect rate under varying levels noise. We observed robustness noise, as decreases relatively slowly increasing Statistical analysis obtained experimental results confirmed new state-of-the-art for datasets whose target variable modelled closed-form equation allowed operators. addition evaluation known datasets, introduced randomly generated set instances. The goal instances test sensitivity our incremental sizes different have also proposed parallelized extension RILS-ROLS proven adequate several very large 1 million records up 15 input variables.
منابع مشابه
Robust linear least squares regression
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau...
متن کاملRobust and Efficient Subspace Segmentation via Least Squares Regression
This paper studies the subspace segmentation problem which aims to segment data drawn from a union of multiple linear subspaces. Recent works by using sparse representation, low rank representation and their extensions attract much attention. If the subspaces from which the data drawn are independent or orthogonal, they are able to obtain a block diagonal affinity matrix, which usually leads to...
متن کاملLinear regression for numeric symbolic variables: an ordinary least squares approach based on Wasserstein Distance
In this paper we present a linear regression model for modal symbolic data. The observed variables are histogram variables according to the definition given in Bock and Diday [1] and the parameters of the model are estimated using the classic Least Squares method. An appropriate metric is introduced in order to measure the error between the observed and the predicted distributions. In particula...
متن کاملC Ordinary Least Squares and Poisson Regression Models
With an assumption of normality for the regression error term, OLS also corresponds to Maximum Likelihood (ML) estimation. The note contains the statistical model and all expressions that are needed to carry out estimation and essential model diagnostics. Both concise matrix notation as well as more extensive full summation notation are employed, to provide a direct link to “loop” structures in...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Big Data
سال: 2023
ISSN: ['2196-1115']
DOI: https://doi.org/10.1186/s40537-023-00743-2