Performance and optimization of support vector machines in high-energy physics classification problems

Sahin, Mehmet Özgür; Krücker, D.; Melzer-Pellmann, I.-A.

doi:10.1016/j.nima.2016.09.017

Cited by 11 publications

(4 citation statements)

References 45 publications

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“…The distinction is not a single linear equation but a range that can be expressed by many equations [37,38] The SVM algorithm was created by Vladimir Vapnik and Alexey Chervonenkis in 1963 [39]. This method has been used in many fields, such as chemistry [40], physics [41], biology, and technology. To perform the classification process, SVM determines the optimum hyperplane, that is, the decision plane, which separates the classes from each other [42,43].…”

Section: C)mentioning

confidence: 99%

A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain’s Electrical Activity Signals

et al. 2023

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This study uses machine learning to perform the hearing test (audiometry) processes autonomously with EEG signals. Sounds with different amplitudes and wavelengths given to the person tested in standard hearing tests are assigned randomly with the interface designed with MATLAB GUI. The person stated that he heard the random size sounds he listened to with headphones but did not take action if he did not hear them. Simultaneously, EEG (electro-encephalography) signals were followed, and the waves created in the brain by the sounds that the person attended and did not hear were recorded. EEG data generated at the end of the test were pre-processed, and then feature extraction was performed. The heard and unheard information received from the MATLAB interface was combined with the EEG signals, and it was determined which sounds the person heard and which they did not hear. During the waiting period between the sounds given via the interface, no sound was given to the person. Therefore, these times are marked as not heard in EEG signals. In this study, brain signals were measured with Brain Products Vamp 16 EEG device, and then EEG raw data were created using the Brain Vision Recorder program and MATLAB. After the data set was created from the signal data produced by the heard and unheard sounds in the brain, machine learning processes were carried out with the PYTHON programming language. The raw data created with MATLAB was taken with the Python programming language, and after the pre-processing steps were completed, machine learning methods were applied to the classification algorithms. Each raw EEG data has been detected by the Count Vectorizer method. The importance of each EEG signal in all EEG data has been calculated using the TF-IDF (Term Frequency-Inverse Document Frequency) method. The obtained dataset has been classified according to whether people can hear the sound. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms have been applied in the analysis. The algorithms selected in our study were preferred because they showed superior performance in ML and succeeded in analyzing EEG signals. Selected classification algorithms also have features of being used online. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms were used. In the analysis of EEG signals, Light Gradient Strengthening Machine (LGBM) was obtained as the best method. It was determined that the most successful algorithm in prediction was the prediction of the LGBM classification algorithm, with a success rate of 84%. This study has revealed that hearing tests can also be performed using brain waves detected by an EEG device. Although a completely independent hearing test can be created, an audiologist or doctor may be needed to evaluate the results.

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Section: C)mentioning

confidence: 99%

A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain’s Electrical Activity Signals

et al. 2023

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“…After this preselection, the remaining background is still several orders of magnitude higher than the investigated signal. This setup is similar to the one used in [17] where more details can be found. The selected sample, which is used for training and tuning, consists of 1.4 × 10 6 events with a sample composition of 50% signal and 50% background events.…”

Section: An Example Search For a Third-generation Supersymmetric Quar...mentioning

confidence: 99%

“…In [17], it had been argued that optimising for accuracy or AUC is not appropriate for a HEP search. The true positive with respect to the false positive labels are more relevant in this case.…”

Section: Performance Measures Of Machine Learning Techniques Applied ...mentioning

confidence: 99%

Direct optimisation of the discovery significance when training neural networks to search for new physics in particle colliders

Elwood,

Krücker

2018

Preprint

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We introduce two new loss functions designed to directly optimise the statistical significance of the expected number of signal events when training neural networks to classify events as signal or background in the scenario of a search for new physics at a particle collider. The loss functions are designed to directly maximise commonly used estimates of the statistical significance, s/ √ s + b, and the Asimov estimate, Z A . We consider their use in a toy SUSY search with 30 fb −1 of 14 TeV data collected at the LHC. In the case that the search for the SUSY model is dominated by systematic uncertainties, it is found that the loss function based on Z A can outperform the binary cross entropy in defining an optimal search region.

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“…Several reports of SVMs use in HEP have been made: including jet flavour tagging and muon identification [6], top physics [7,8], background suppression for W → eν signatures [9], and SUSY searches [10] at LEP, Tevatron and LHC experiments. Ref.…”

Section: Svmsmentioning

confidence: 99%

Support Vector Machines and generalisation in HEP

Bevan,

Goñi,

Hays

et al. 2017

Preprint

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We review the concept of Support Vector Machines (SVMs) and discuss examples of their use in a number of scenarios. Several SVM implementations have been used in HEP and we exemplify this algorithm using the Toolkit for Multivariate Analysis (TMVA) implementation.We discuss examples relevant to HEP including background suppression for H → τ + τ − at the LHC with several different kernel functions. Performance benchmarking leads to the issue of generalisation of hyper-parameter selection. The avoidance of fine tuning (over training or over fitting) in MVA hyper-parameter optimisation, i.e. the ability to ensure generalised performance of an MVA that is independent of the training, validation and test samples, is of utmost importance. We discuss this issue and compare and contrast performance of hold-out and k-fold cross-validation. We have extended the SVM functionality and introduced tools to facilitate cross validation in TMVA and present results based on these improvements.

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Performance and optimization of support vector machines in high-energy physics classification problems

Cited by 11 publications

References 45 publications

A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain’s Electrical Activity Signals

A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain’s Electrical Activity Signals

Direct optimisation of the discovery significance when training neural networks to search for new physics in particle colliders

Support Vector Machines and generalisation in HEP

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