On Subsampling Procedures for Support Vector Machines

Bárcenas, Roberto; González-Lima, María D.; Quiróz, Adolfo J.

doi:10.3390/math10203776

Cited by 4 publications

(3 citation statements)

References 38 publications

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“…A different approach is presented in [14] where the reduction of the data includes the assignments of large weights to important samples and the reduction of the features, by using graph and self-paced learning. The methods in [6] and [5] use nearest neighbors with sub-sampling in order to select a subset of significant instances. They start by training an SVM formed by a very small sub-samples of the data set.…”

Section: Related Workmentioning

confidence: 99%

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A Graph Classification Method Based on Support Vector Machines and Locality-Sensitive Hashing

Gonzalez-Lima,

Ludeña,

Otazo-Sanchez

2024

IEEE Access

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Graphs classification is a relevant problem that arises in many disciplines. Using graphs directly instead of vectorization allows to exploit the intrinsic representations of the data. Support Vector Machines (SVM) is a supervised learning method based on the use of graph kernel functions used for this task. One of the problems of SVM, as the number of samples increases, is the cost of storing and solving the optimization problem related to SVM. In this work we propose a method capable of finding a small representative subset of the whole graph data set such that an approximate solution of the SVM optimization problem can be obtained in a fraction of the time, and without significantly degrading the classification prediction error. The method is based on the use of Locality-Sensitive Hashing for projecting over the Hilbert spaces defined by appropriate graph kernels that measure similarity between the graphs. A description of the algorithm, as well as numerical results using two graph kernels (Simple Product and Random Walk) on simulated and real life data sets are presented. The numerical experiments compare the training times and the classification error between the SVM obtained with our smart sampling approach, and the SVM obtained over the complete data set or over a random sub-sample. The results offer evidence of the advantages of our proposal for solving large scale graph classification problems when using SVM.

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Section: Related Workmentioning

confidence: 99%

“…This process is repeated until the classification error becomes almost constant. In [5] the authors also provide a theoretical framework for the analysis of random sub-sampling when using SVM.…”

Section: Related Workmentioning

confidence: 99%

A Graph Classification Method Based on Support Vector Machines and Locality-Sensitive Hashing

Gonzalez-Lima,

Ludeña,

Otazo-Sanchez

2024

IEEE Access

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“…Support Vector Machine is a widely used technique that applied in many real-world applications (Bárcenas et al 2022). The theoretical basis for classi cation method of SVM is similar to that for Support Vector Regression (SVR), with slight changes.…”

Section: Support Vector Machinementioning

confidence: 99%

Predicting reference evapotranspiration in semi-arid-region by regression- based machine learning methods using limited climatic inputs

Sammen

Kişi

Al‐Janabi

et al. 2023

Preprint

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Accurately estimation of evapotranspiration is very essential for water resources planning and management projects. In this study, different regression-based machine learning techniques including support vector machine (SVM), random forest (RF), Bagged trees algorithm (BaT) and Boosting trees algorithm (BoT) were adopted in order to model daily reference evapotranspiration (ET0) for semi-arid region. Five stations in Hemren catchment basin located at the North-East part of Iraq were selected as case study. Several climatic parameters including solar radiation (SR), wind speed (Us), relative humidity (RH), and maximum and minimum air temperatures (Tmax and Tmin) of 35 years (1979–2014) period were used as inputs to the models. Assessment of the methods with various input combinations indicated that the RF method especially with Tmax, Tmin, Tmean & SR inputs provided the best accuracy in estimating daily ET0 in all stations. It was followed by the BaT and BoT methods while the SVM had the worst accuracy. In some cases, 1st input scenario (Tmax, Tmin, Tmean, SR, WS and RH) provided slightly better accuracy than the 2nd input scenario (Tmax, Tmin, Tmean & SR).

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Optimizing Machine Learning Models with Data-level Approximate Computing: The Role of Diverse Sampling, Precision Scaling, Quantization and Feature Selection Strategies

Dalloo,

Humaidi

2024

Results in Engineering

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On Subsampling Procedures for Support Vector Machines

Cited by 4 publications

References 38 publications

A Graph Classification Method Based on Support Vector Machines and Locality-Sensitive Hashing

A Graph Classification Method Based on Support Vector Machines and Locality-Sensitive Hashing

Predicting reference evapotranspiration in semi-arid-region by regression- based machine learning methods using limited climatic inputs

Optimizing Machine Learning Models with Data-level Approximate Computing: The Role of Diverse Sampling, Precision Scaling, Quantization and Feature Selection Strategies

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