KNN-based least squares twin support vector machine for pattern classification

Mir, Amir M.; Nasiri, Jalal A.

doi:10.1007/s10489-018-1225-z

Cited by 29 publications

(8 citation statements)

References 29 publications

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“…For example, PLS-TSVM has the rank of 3 in terms of the prediction accuracy among the four SVM-based algorithms using Balance dataset. The average of these ranks have been reported as the overall rank in the last row of the Tables 2 and 3 [30]. As can be seen in both tables, our proposed method obtained the least rank score among the three other algorithms.…”

Section: Uci Data Setsmentioning

confidence: 90%

“…Although much research based on least square twin support vector machine has been presented [28][29][30][31][32][33] they are incapable of eliminating the consequences of unclassifiable regions. Therefore as it was also mentioned previously, our motivation in this research is to propose a classifier based on LS-TSVM that addresses the unclassifiable regions (URs).…”

Section: Probabilistic Least Square Twin Support Vector Machinementioning

confidence: 99%

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Probabilistic Twin Support Vector Machine for Solving Unclassiﬁable Region Problem

Shakibian

Nasiri

2022

IJE

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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.

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Section: Uci Data Setsmentioning

confidence: 90%

Section: Probabilistic Least Square Twin Support Vector Machinementioning

confidence: 99%

Probabilistic Twin Support Vector Machine for Solving Unclassiﬁable Region Problem

Shakibian

Nasiri

2022

IJE

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“…By combining the characteristics of the fish body shape and the improved U-net to obtain a precise binary segmentation image, the idea of microscopicizing the fish body into multiple pixels is proposed. The least squares method [42,43] is used to fit the point set in a straight line, and the angle of the straight line can be regarded as the angle of the fish. At the same time, the method also conforms to the judgment logic of human judging the tilt direction of fish.…”

Section: Line Fitting Schemementioning

confidence: 99%

Intelligent Measurement of Morphological Characteristics of Fish Using Improved U-Net

Han

et al. 2021

Electronics

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In the smart mariculture, batch testing of breeding traits is a key issue in the breeding of improved fish varieties. The body length (BL), body width (BW) and body area (BA) features of fish are important indicators. They are of great significance in breeding, feeding and classification. To accurately and intelligently obtain the morphological characteristic sizes of fish in actual scenes, data augmentation is first used to greatly expand the published fish dataset, thereby ensuring the robustness of the training model. Then, an improved U-net segmentation and measurement algorithm is proposed, which uses a dilated convolution with a dilation rate 2 and a convolution to partially replace the convolution in the original U-net. This operation can enlarge the partial convolution receptive field and achieve more accurate segmentation for large targets in the scene. Finally, a line fitting method based on the least squares method is proposed, which is combined with the body shape features of fish and can accurately measure the BL and BW of inclined fish. Experimental results show that the Mean Intersection over Union (mIoU) is 97.6% and the average relative error of the area is 0.69%. Compared with the unimproved U-net, the average relative error of the area is reduced to about half. Moreover, with the improved U-net and the line fitting method, the average relative error of BL and the average relative error of BW of inclined fish decrease to 0.37% and 0.61%, respectively.

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“…Moreover, this software can be applied to a wide variety of research applications such as text classification, image or video recognition, medical diagnosis, and bioinformatics. For example, Light TwinSVM was used for the numerical experiments in our previous research paper (Mir & Nasiri, 2018).…”

Section: Lighttwinsvmmentioning

confidence: 99%

LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier

Mir¹,

Nasiri²

2019

JOSS

Self Cite

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KNN-based least squares twin support vector machine for pattern classification

Cited by 29 publications

References 29 publications

Probabilistic Twin Support Vector Machine for Solving Unclassiﬁable Region Problem

Probabilistic Twin Support Vector Machine for Solving Unclassiﬁable Region Problem

Intelligent Measurement of Morphological Characteristics of Fish Using Improved U-Net

LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier

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