Noncost Sensitive SVM Training Using Multiple Model Selection

Chatelain, Clément; Adam, Sébastien; Lecourtier, Yves; Paquet, Thierry

doi:10.1142/s0218126610005937

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…The technique in [35] uses meta-learning, case-based reasoning to propose good staring points for evolutionary parameter optimization of SVM. The study in [36] presented the multi-objective optimization framework for tuning SVM hyper-parameters. More recent research [37] proposed the method for automatically picking out the kernel type (linear/non-linear) and tuning parameters.…”

Section: Discussion On Related Workmentioning

confidence: 99%

Automatic Learning Algorithms for Local Support Vector Machines

2019

SN COMPUT. SCI.

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In this paper, we propose automatic learning algorithms (called auto-kSVM-1p, autokSVM-dp) being possible to automatically tune hyper-parameters of k local support vector machines (SVM) for classifying large data sets. The autokSVM algorithms are able to determine the number of clusters k to partition the large training data, followed which they auto-learn the non-linear SVM model in each cluster to classify the data locally in the parallel way on multi-core computers. The autokSVM algorithms combine the grid search, the .632 bootstrap estimator, the hill climbing heuristic to optimize hyper-parameters in the local non-linear SVM training. The numerical test results on four data sets from UCI repository and three benchmarks of handwritten letters recognition showed that our autokSVM algorithms give very competitive classification results compared to the standard LibSVM and the original kSVM. An example of autokSVM-1p's effectiveness is given with an accuracy of 96.74% obtained in the classification of Forest cover-type data set having 581,012 datapoints in 54-dimensional input space and 7 classes in 334.45 seconds using a PC Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores. Keywords Support vector machines • Large data sets • Local support vector machines • Automatic hyper-parameter tuning This article is part of the topical collection "Future Data and Security Engineering" guest edited by Tran Khanh Dang.

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Section: Discussion On Related Workmentioning

confidence: 99%

Automatic Learning Algorithms for Local Support Vector Machines

2019

SN COMPUT. SCI.

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Automatic Hyper-parameters Tuning for Local Support Vector Machines

Tran-Nguyen

2018

Future Data and Security Engineering

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A Fault Diagnosis Method Based on Active Example Selection

Zhao

Wang

2017

J CIRCUIT SYST COMP

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The fault diagnosis in the real world is often complicated. It is due to the fact that not all relevant fault information is available directly. In many fault diagnosis situations, it is impossible or inconvenient to find all fault information before establishing a fault diagnosis model. To deal with this issue, a method named active example selection (AES) is proposed for the fault diagnosis. AES could actively discover unseen faults and choose useful samples to improve the fault detection accuracy. AES consists of three key components: (1) a fusion model of combining the advantage of the unsupervised and supervised fault diagnosis methods, where the unsupervised fault diagnosis methods could discover unseen faults and the supervised fault diagnosis methods could provide better fault detection accuracy on seen faults, (2) an active learning algorithm to help the supervised fault diagnosis methods actively discover unseen faults and choose useful samples to improve the fault detection accuracy, and (3) an incremental learning scheme to speed up the iterative training procedure for AES. The proposed method was evaluated on the benchmark Tennessee Eastman Process data. The proposed method performed better on both unseen and seen faults than the stand-alone unsupervised, supervised fault diagnosis methods, their joint and referenced support vector machines based on active learning.

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Noncost Sensitive SVM Training Using Multiple Model Selection

Cited by 3 publications

References 13 publications

Automatic Learning Algorithms for Local Support Vector Machines

Automatic Learning Algorithms for Local Support Vector Machines

Automatic Hyper-parameters Tuning for Local Support Vector Machines

A Fault Diagnosis Method Based on Active Example Selection

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