A Genetic Algorithm Based Modification on the LTS Algorithm for Large Data Sets

Satman, Mehmet Hakan

doi:10.1080/03610918.2011.598989

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…GALTS draws random candidate solutions (or chromosomes) for which search methods are appropriate for use in nonlinear or non-differentiable optimization problems [32]. Genetic Algorithms (GAs) perform a parallel search to cope with the local optima problem.…”

Section: Genetic Algorithm Using Least Trimmed Square (Galts)mentioning

confidence: 99%

“…External parameter orthogonalization, coupled with RFs, SVM, partial least squares regression and ANN models, was applied on a larger soil database and satisfactory results were obtained. With the advancement in machine learning models, the Genetic Algorithm coupled with Least Trimmed Squares (GALTS) [32] has been developed, which uses a small number of trials to achieve the objective functions; as a result, it reduces the variance and biases in the datasets.…”

Section: Introductionmentioning

confidence: 99%

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Random Forests with Bagging and Genetic Algorithms Coupled with Least Trimmed Squares Regression for Soil Moisture Deficit Using SMOS Satellite Soil Moisture

Srivastava

Petropoulos

Prasad

et al. 2021

IJGI

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Soil Moisture Deficit (SMD) is a key indicator of soil water content changes and is valuable to a variety of applications, such as weather and climate, natural disasters, agricultural water management, etc. Soil Moisture and Ocean Salinity (SMOS) is a dedicated mission focused on soil moisture retrieval and can be utilized for SMD estimation. In this study, the use of soil moisture derived from SMOS has been provided for the estimation of SMD at a catchment scale. Several approaches for the estimation of SMD are implemented herein, using algorithms such as Random Forests (RF) and Genetic Algorithms coupled with Least Trimmed Squares (GALTS) regression. The results show that for SMD estimation, the RF algorithm performed best as compared to the GALTS, with Root Mean Square Errors (RMSEs) of 0.021 and 0.024, respectively. All in all, our study findings can provide important assistance towards developing the accuracy and applicability of remote sensing-based products for operational use.

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Section: Genetic Algorithm Using Least Trimmed Square (Galts)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Random Forests with Bagging and Genetic Algorithms Coupled with Least Trimmed Squares Regression for Soil Moisture Deficit Using SMOS Satellite Soil Moisture

Srivastava

Petropoulos

Prasad

et al. 2021

IJGI

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“…[25] devised an algorithm for estimating LTS for large datasets. [27] extended this algorithm using genetic algorithms based initial subset selection.…”

Section: Introductionmentioning

confidence: 99%

Fast online detection of outliers using least-trimmed squares regression with non-dominated sorting based initial subsets

Satman

2015

IJASP

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In this paper, a new algorithm is devised for calculating the Least Trimmed of Squares (LTS) estimator. The algorithm consists of two steps. In the first step, the non-dominated sorting algorithm is applied on the design matrix of regression data for selecting a clean subset of observations. In the second step, C-steps are iterated to adjust the LTS estimators. The algorithm is fast and precise for small sample sizes, however, the sorting algorithm is computationally inefficient in large datasets. A fast update mechanism can be used in online data with a linear increase in computation time. Some properties of the sorting algorithm are also investigated under some transformations. Results of applying the algorithm on some well-known datasets and Monte Carlo simulations show that the proposed algorithm is suitable to use in many cases when the computation time is the major objective and a moderate level of precision is enough.

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“…Rousseeuw and van Driessen (2006) suggested a new algorithm for calculating the LTS estimator for large data sets. Satman (2012) proposed a genetic algorithm (GA) based modification on the method given in Rousseeuw and van Driessen (2006) and showed that the GA based search obtains smaller objective values in reasonable CPU times. Torti et al (2012) performed a simulation study to compare powers of fast robust regression estimators including forward seach (Atkinson, 2010).…”

Section: Introductionmentioning

confidence: 99%

A New Algorithm for Detecting Outliers in Linear Regression

Satman¹

2013

IJSP

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In this paper, we present a new algorithm for detecting multiple outliers in linear regression. The algorithm is based on a non-iterative robust covariance matrix and concentration steps used in LTS estimation. A robust covariance matrix is constructed to calculate Mahalanobis distances of independent variables which are then used as weights in weighted least squares estimation. A few concentration steps are then performed using the observations that have smallest residuals. We generate random data sets for n = 10 3 , 10 4 , 10 5 and p = 5, 10 to show up the capabilities of the algorithm. In our Monte Carlo simulations, it is shown that our algorithm has very low masking and swamping ratios when the number of observations is up to 10 4 in the case of maximum contamination in X-Space. It is also shown that, the algorithm is successful in the case of Y-Space outliers when the contamination level, sample size and number of parameters are up to 30%, n = 10 5 , and p = 10, respectively. Bias, variance and MSE statistics are calculated for different scenarios. The reported computation time of our implementation is quite short. It is concluded that the presented algorithm is suitable and applicable for detecting multiple outliers in regression analysis with its small masking and swamping ratios, accurate estimates of regression parameters except the intercept, and short computation time in large data sets and high level of contamination. A future work is required for reducing bias and variance of the intercept estimator in the model.

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A Genetic Algorithm Based Modification on the LTS Algorithm for Large Data Sets

Cited by 5 publications

References 14 publications

Random Forests with Bagging and Genetic Algorithms Coupled with Least Trimmed Squares Regression for Soil Moisture Deficit Using SMOS Satellite Soil Moisture

Random Forests with Bagging and Genetic Algorithms Coupled with Least Trimmed Squares Regression for Soil Moisture Deficit Using SMOS Satellite Soil Moisture

Fast online detection of outliers using least-trimmed squares regression with non-dominated sorting based initial subsets

A New Algorithm for Detecting Outliers in Linear Regression

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