A Multiclass Nonparallel Parametric-Margin Support Vector Machine

The twin parametric-margin support vector machine (TPMSVM) is an excellent kernel-based nonparallel classifier. However, TPMSVM was originally designed for binary classification, which is unsuitable for real-world multiclass applications. Therefore, this paper extends TPMSVM for multiclass classification and proposes a novel K multiclass nonparallel parametric-margin support vector machine (MNP-KSVC). Specifically, our MNP-KSVC enjoys the following characteristics. (1) Under the “one-versus-one-versus-rest” multiclass framework, MNP-KSVC encodes the complicated multiclass learning task into a series of subproblems with the ternary output {−1,0,+1}. In contrast to the “one-versus-one” or “one-versus-rest” strategy, each subproblem not only focuses on separating the two selected class instances but also considers the side information of the remaining class instances. (2) MNP-KSVC aims to find a pair of nonparallel parametric-margin hyperplanes for each subproblem. As a result, these hyperplanes are closer to their corresponding class and at least one distance away from the other class. At the same time, they attempt to bound the remaining class instances into an insensitive region. (3) MNP-KSVC utilizes a hybrid classification and regression loss joined with the regularization to formulate its optimization model. Then, the optimal solutions are derived from the corresponding dual problems. Finally, we conduct numerical experiments to compare the proposed method with four state-of-the-art multiclass models: Multi-SVM, MBSVM, MTPMSVM, and Twin-KSVC. Experimental results demonstrate the feasibility and effectiveness of MNP-KSVC in terms of multiclass accuracy and learning time.

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Short-Term Traffic Flow Forecasting Based on Data-Driven Model

Mathematics ◽

10.3390/math8020152 ◽

2020 ◽

Vol 8 (2) ◽

pp. 152 ◽

Cited By ~ 2

Author(s):

Su-qi Zhang ◽

Kuo-Ping Lin

Keyword(s):

Support Vector Machine ◽

Traffic Flow ◽

Extreme Learning Machine ◽

Support Vector ◽

Local Optimum ◽

Short Term ◽

Swarm Optimization ◽

Traffic Flow Forecasting ◽

Learning Machine ◽

The One

Short-term traffic flow forecasting is the technical basis of the intelligent transportation system (ITS). Higher precision, short-term traffic flow forecasting plays an important role in alleviating road congestion and improving traffic management efficiency. In order to improve the accuracy of short-term traffic flow forecasting, an improved bird swarm optimizer (IBSA) is used to optimize the random parameters of the extreme learning machine (ELM). In addition, the improved bird swarm optimization extreme learning machine (IBSAELM) model is established to predict short-term traffic flow. The main researches in this paper are as follows: (1) The bird swarm optimizer (BSA) is prone to fall into the local optimum, so the distribution mechanism of the BSA optimizer is improved. The first five percent of the particles with better fitness values are selected as producers. The last ten percent of the particles with worse fitness values are selected as beggars. (2) The one-day and two-day traffic flows are predicted by the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM), bird swarm optimization extreme learning machine (BSAELM) and IBSAELM models, respectively. (3) The prediction results of the models are evaluated. For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the IBSAELM model are smaller than the SVM, PSOSVM and BSAELM models, respectively. The experimental analysis results show that the IBSAELM model proposed in this study can meet the actual engineering requirements.

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A 1-norm regularized linear programming nonparallel hyperplane support vector machine for binary classification problems

Neurocomputing ◽

10.1016/j.neucom.2019.09.068 ◽

2020 ◽

Vol 376 ◽

pp. 141-152 ◽

Cited By ~ 2

Author(s):

Qianqian Zhang ◽

Haifeng Wang ◽

Sang Won Yoon

Keyword(s):

Support Vector Machine ◽

Linear Programming ◽

Binary Classification ◽

Support Vector ◽

Classification Problems

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An Extension of the VSM Documents Representation using Word Embedding

Balkan Region Conference on Engineering and Business Education ◽

10.1515/cplbu-2017-0033 ◽

2017 ◽

Vol 2 (1) ◽

pp. 249-257

Author(s):

Daniel Morariu ◽

Lucian Vințan ◽

Radu Crețulescu

Keyword(s):

Support Vector Machine ◽

Natural Language Processing ◽

Language Processing ◽

Learning Algorithm ◽

Word Embedding ◽

Support Vector ◽

Science Students ◽

Document Representation ◽

Learning Time ◽

Educational Paradigm

Abstract In this paper, we will present experiments that try to integrate the power of Word Embedding representation in real problems for documents classification. Word Embedding is a new tendency used in the natural language processing domain that tries to represent each word from the document in a vector format. This representation embeds the semantically context in that the word occurs more frequently. We include this new representation in a classical VSM document representation and evaluate it using a learning algorithm based on the Support Vector Machine. This new added information makes the classification to be more difficult because it increases the learning time and the memory needed. The obtained results are slightly weaker comparatively with the classical VSM document representation. By adding the WE representation to the classical VSM representation we want to improve the current educational paradigm for the computer science students which is generally limited to the VSM representation.

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