A Multi-Class Classification Weighted Least Squares Twin Support Vector Hypersphere Using Local Density Information

Ai, Qing; Zhang, Aihua; Wang, Yang; Sun, Hao

doi:10.1109/access.2018.2815707

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…In general, One-versus-One TSVMs have higher performance. Ai et al [2] in 2018 proposed a multi-class classification weighted least squares TSVH using local density information in order to improve the performance of LSTSVH. Authors introduced local density information into LSTSVH to provide weight for each data point in order to avoid noise sensitivity.…”

Section: Twin Support Vector Machine For Multi-class Classificationmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

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Twin support vector machine (TSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with we first introduce the basic theory of TSVM and then focus on the various improvements and applications of TSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.

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Section: Twin Support Vector Machine For Multi-class Classificationmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

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“…Considering the above problems, we design a patches selection function that selects three samples from a lung nodule patch sequence to represent the density distribution of this nodule. The function is shown in (34) - (36):…”

Section: ) Texture Feature Extractionmentioning

confidence: 99%

“…Then, Tex i (i = 1, 2, 3) are used together for texture extraction in the subsequent steps. It can be seen from the (34)- (36) that Tex 1 , Tex 2 , and Tex 3 are indexes of the patches having the largest area in the front, middle and back parts of the sequence, respectively. This ensures these patches containing identifiable texture information and we can obtain the density distribution from them.…”

Section: ) Texture Feature Extractionmentioning

confidence: 99%

“…In this work, we select three classification methods i.e., NNCS [35], MWLSTSVH [36] and DLSR [37], to divide the joint feature vector of EOH and MSPLBP. These classification results are used to evaluate the performance of our system.…”

Section: Classificationmentioning

confidence: 99%

“…Considering this problem, MSPLBP expands the lateral scale to improve the description of the texture size and the texture structure, and merges the paths to reduce the feature dimensions. Finally, three state-of-the-art classifiers i.e., NNCS [35], MWLSTSVH [36], and DLSR [37] are used to classify the fused spatial edge and texture feature of the 3D lung entities into 6 malignancy levels.…”

Section: Introductionmentioning

confidence: 99%

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A Fast and Efficient CAD System for Improving the Performance of Malignancy Level Classification on Lung Nodules

Wang

Cui

et al. 2020

IEEE Access

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Accurate malignancy level classification of lung nodules can reduce lung cancer mortality rate effectively. In this study, we present a fast and efficient CAD system to improve the performance of nodules malignancy level classification. Firstly, to reduce false positives (FPs), we propose a novel vessel segmentation method which measures vessel likelihood by tubular-like structures discriminating from multiple views. The method can recognize irregular vascular structures robustly and sensitively, and achieve fast vessel segmentation. In addition, a mathematical description for 3D pulmonary entities using neighbor centroids clustering is provided as a fundamental condition for spatial feature extraction. To optimize features extraction, we formulate a gray values cumulative function and a patches selection function based on the mathematical description, to generate axial spatial outline and spatial density distribution samples of the entities, respectively. Then, we use Edge Orientation Histogram (EOH) to extract edge features from the spatial outline and propose a multi-scale path LBP (MSPLBP) to extract the texture feature of the density distribution samples. Finally, the fused EOH and MSPLBP are classified into 6 malignancy levels by three state-of-the-art classifiers. The experimental results show that the vessel segmentation method achieves an average F1_Score of 78.14% and AUC value under PR curves of 0.8149. Moreover, our system reaches an average accuracy of 95.88% and consumes average 176.26 seconds for evaluating a CT set on malignancy level classification. These results indicate that the system can segment vessels exactly, and classify the malignancy level of nodules efficiently. Our system is the potential to be a powerful tool for early diagnosis of lung cancer.

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