A comparison study: Support vector machines for binary classification in machine learning

2011 ◽  
Author(s):  
Wencai Zeng ◽  
Jiong Jia ◽  
Zhonglong Zheng ◽  
Chenmao Xie ◽  
Li Guo
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Binary Classification ◽  
Support Vector ◽  
Comparison Study ◽  
Vector Machines ◽  
Study Support

scholarly journals Rescale-Invariant SVM for Binary Classification

2017 ◽  
Author(s):  
Mojtaba Montazery ◽  
Nic Wilson
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Support Vector Machines ◽  
Binary Classification ◽  
Experimental Results ◽  
Support Vector ◽  
Computation Method ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vector Machines

Support Vector Machines (SVM) are among the most well-known machine learning methods, with broad use in different scientific areas. However, one necessary pre-processing phase for SVM is normalization (scaling) of features, since SVM is not invariant to the scales of the features’ spaces, i.e., different ways of scaling may lead to different results. We define a more robust decision-making approach for binary classification, in which one sample strongly belongs to a class if it belongs to that class for all possible rescalings of features. We derive a way of characterising the approach for binary SVM that allows determining when an instance strongly belongs to a class and when the classification is invariant to rescaling. The characterisation leads to a computation method to determine whether one sample is strongly positive, strongly negative or neither. Our experimental results back up the intuition that being strongly positive suggests stronger confidence that an instance really is positive.

Gaussian Processes for Classification: Mean-Field Algorithms

2000 ◽  
Vol 12 (11) ◽  
pp. 2655-2684 ◽  
Author(s):  
Manfred Opper ◽  
Ole Winther
Keyword(s):  
Support Vector Machines ◽  
Gaussian Processes ◽  
Disordered Systems ◽  
Binary Classification ◽  
Computational Cost ◽  
Mean Field ◽  
Strong Support ◽  
Support Vector ◽  
Vector Machines ◽  
Leave One Out

We derive a mean-field algorithm for binary classification with gaussian processes that is based on the TAP approach originally proposed in statistical physics of disordered systems. The theory also yields an approximate leave-one-out estimator for the generalization error, which is computed with no extra computational cost. We show that from the TAP approach, it is possible to derive both a simpler “naive” mean-field theory and support vector machines (SVMs) as limiting cases. For both mean-field algorithms and support vector machines, simulation results for three small benchmark data sets are presented. They show that one may get state-of-the-art performance by using the leave-one-out estimator for model selection and the built-in leave-one-out estimators are extremely precise when compared to the exact leave-one-out estimate. The second result is taken as strong support for the internal consistency of the mean-field approach.

scholarly journals Research on Parallel Support Vector Machine Based on Spark Big Data Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yao Huimin
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Support Vector Machines ◽  
Cross Validation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lambda Architecture ◽  
Vector Machines ◽  
Data Platform

With the development of cloud computing and distributed cluster technology, the concept of big data has been expanded and extended in terms of capacity and value, and machine learning technology has also received unprecedented attention in recent years. Traditional machine learning algorithms cannot solve the problem of effective parallelization, so a parallelization support vector machine based on Spark big data platform is proposed. Firstly, the big data platform is designed with Lambda architecture, which is divided into three layers: Batch Layer, Serving Layer, and Speed Layer. Secondly, in order to improve the training efficiency of support vector machines on large-scale data, when merging two support vector machines, the “special points” other than support vectors are considered, that is, the points where the nonsupport vectors in one subset violate the training results of the other subset, and a cross-validation merging algorithm is proposed. Then, a parallelized support vector machine based on cross-validation is proposed, and the parallelization process of the support vector machine is realized on the Spark platform. Finally, experiments on different datasets verify the effectiveness and stability of the proposed method. Experimental results show that the proposed parallelized support vector machine has outstanding performance in speed-up ratio, training time, and prediction accuracy.

scholarly journals Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

scholarly journals Bankruptcy Prediction of Engineering Companies in the EU Using Classification Methods

2018 ◽  
Vol 66 (5) ◽  
pp. 1347-1356 ◽  
Author(s):  
Michaela Staňková ◽  
David Hampel
Keyword(s):  
Logistic Regression ◽  
Support Vector Machines ◽  
Binary Classification ◽  
Classification Tree ◽  
Classification Trees ◽  
Bankruptcy Prediction ◽  
Support Vector ◽  
Type I ◽  
Vector Machines ◽  
The Eu

This article focuses on the problem of binary classification of 902 small- and medium‑sized engineering companies active in the EU, together with additional 51 companies which went bankrupt in 2014. For classification purposes, the basic statistical method of logistic regression has been selected, together with a representative of machine learning (support vector machines and classification trees method) to construct models for bankruptcy prediction. Different settings have been tested for each method. Furthermore, the models were estimated based on complete data and also using identified artificial factors. To evaluate the quality of prediction we observe not only the total accuracy with the type I and II errors but also the area under ROC curve criterion. The results clearly show that increasing distance to bankruptcy decreases the predictive ability of all models. The classification tree method leads us to rather simple models. The best classification results were achieved through logistic regression based on artificial factors. Moreover, this procedure provides good and stable results regardless of other settings. Artificial factors also seem to be a suitable variable for support vector machines models, but classification trees achieved better results using original data.

scholarly journals On the parameter optimization of Support Vector Machines for binary classification

2012 ◽  
Vol 9 (3) ◽  
pp. 33-43 ◽  
Author(s):  
Paulo Gaspar ◽  
Jaime Carbonell ◽  
José Luís Oliveira
Keyword(s):  
Support Vector Machines ◽  
Binary Classification ◽  
Classification Performance ◽  
Biological Data ◽  
Parameters Optimization ◽  
Support Vector ◽  
Minimal Risk ◽  
Class Separation ◽  
Vector Machines ◽  
Analyse Data

Summary Classifying biological data is a common task in the biomedical context. Predicting the class of new, unknown information allows researchers to gain insight and make decisions based on the available data. Also, using classification methods often implies choosing the best parameters to obtain optimal class separation, and the number of parameters might be large in biological datasets.Support Vector Machines provide a well-established and powerful classification method to analyse data and find the minimal-risk separation between different classes. Finding that separation strongly depends on the available feature set and the tuning of hyper-parameters. Techniques for feature selection and SVM parameters optimization are known to improve classification accuracy, and its literature is extensive.In this paper we review the strategies that are used to improve the classification performance of SVMs and perform our own experimentation to study the influence of features and hyper-parameters in the optimization process, using several known kernels.

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