بحث بعنوان Probabilistic Twin Support Vector Machine for Solving Unclassifiable Region Problem
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 بحث بعنوان Probabilistic Twin Support Vector Machine for Solving Unclassifiable Region Problem

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بحث بعنوان
Probabilistic Twin Support Vector Machine for Solving Unclassifiable Region Problem
J. A. Nasiria, H. Shakibian*b
a Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
b Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran

بحث بعنوان Probabilistic Twin Support Vector Machine for Solving Unclassifiable Region Problem  P_t_s_10
و المحتوى كما يلي :


P A P E R I N F O
Paper history:
Received 25 August 2021
Received in revised form 07 October 2021
Accepted 11 October 2021
Keywords:
Probabilistic Twin Support Vector Machine
Unclassifiable Region
Multi-class Classification
Human Action Recognition
A B S T R A C T
Support Vector Machine classifiers are widely used in many classification tasks. However, they have
two considerable weaknesses, Unclassifiable Region (UR) in multiclass classification and outliers. In
this research, we address these problems by introducing Probabilistic Least Square Twin Support Vector
Machine (PLS-TSVM). The proposed algorithm introduces continuous and probabilistic outputs over
the model obtained by Least-Square Twin Support Vector Machine (LS-TSVM) method with both linear
and polynomial kernel functions. PLS-TSVM not only solves the unclassifiable region problem by
introducing a continuous output value membership function, but it also reduces the adverse effects of
noisy data and outliers. For showing the superiority of our proposed method, we have conducted
experiments on various UCI datasets. In the most cases, higher or competitive accuracy to other methods
have been obtained such that in some UCI datasets, PLS-TSVM could obtain up to 99.90% of
classification accuracy. Moreover, PLS-TSVM has been evaluated against ”one-against-all” and ”oneagainst-one” approaches on several well-known video datasets such as Weizmann, KTH, and UCF101
for human action recognition task. The results show the higher accuracy of PLS-TSVM compared to its
counterparts. Specifically, the proposed algorithm could improve respectively about 12.2%, 2.8%, and
12.1% of classification accuracy in three video datasets compared to the standard SVM and LS-TSVM
classifiers. The final results indicate that the proposed algorithm could achieve better overall
performances than the literature.
CONCLUSION
In this paper, Probabilistic Least Square Twin Support
Vector Machine (PLS-TSVM) has been introduced. PLSTSVM addressed several problems that may occur in
TSVM-based algorithms such as unclassifiable regions
(URs) and their sensitivity to outliers when they are
applied to multiclass classification tasks such as human
activity recognition. PLS-TSVM classifier performs
classification by the use of two nonparallel hyperplanes
similar to TSVM, unlike SVM, which uses a single
hyperplane. Finally, a continuous output value is defined
by comparing the distances between the samples and two
separating hyperplanes to handle URs. In this research,
we had two approaches to evaluate our proposed method.
We first conducted experiments with PLS-TSVM on a set
of UCI data sets and compared the results with SVM,
TSVM, and LS-TSVM. Then we applied PLS-TSVM to
3 well-known human action video data sets and provided
the results to be able to compare with the literature. For
these experiments, we have used the HOG/HOF
descriptor to present each video sequence in the bag of
words (BoW) model. The results indicate that our
proposed PLS-TSVM reaches a better performance on
UCI data sets compared to the other three algorithms and
also produces a significant improvement in action
recognition while the computational time of the method
is several orders of magnitude faster than SVM and
AdaBoost classification based methods.


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