Constructing ECOC based on confusion matrix for multiclass learning problems

Zhou, Jing; Ye, Yun; Zhang, Mingjie; Xing, Hanyu

doi:10.1007/s11432-015-5321-y

Cited by 13 publications

(10 citation statements)

References 11 publications

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“…However, classification accuracy cannot reflect the classification accuracy of minority classes [ 58 ]. The form of the confusion matrix is generally adopted in imbalance classification [ 59 ]. Table 6 shows the confusion matrix commonly used to measure the effectiveness of algorithms in unbalanced data tasks.…”

Section: Resultsmentioning

confidence: 99%

Forecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection

Zhang

2021

PLoS ONE

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Traditional forecasting methods in mergers and acquisitions (M&A) data have two limitations that significantly reduce forecasting accuracy: (1) the imbalance of data, that is, the failure cases of M&A are far fewer than the successful cases (82%/18% of our sample), and (2) both the bidder and the target of the merger have numerous descriptive features, making it difficult to choose which ones to forecast. This study proposes a neural network using partial-sigmoid (i.e., partial-sigmoid neural network [PSNN]) as the activation function of the output layer and compares three feature selection methods, namely, chi-square (chi2) test, information gain and gradient boosting decision tree (GBDT). Experimental results prove that our PSNN (improved up to 0.37 precision, 0.49 recall, 0.41 G-Mean and 0.23 F1-measure) and feature selection (improved 1.83%-13.16% accuracy) method can effectively improve the adverse effects of the defects of the above two merger data on forecasting. Scholars who studied the forecast of merger failure have overlooked three important features: assets of the previous year, market value and capital expenditure. The chi2 test feature selection method is the best among the three feature selection methods.

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Section: Resultsmentioning

confidence: 99%

Forecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection

Zhang

2021

PLoS ONE

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“…2. Error correcting output code (ECOC): ECOC is a meta-method which is used for multi-class classification problem [47]. To solve the multi-class problem, ECOC combines many binary-SVM classifiers deal with multiple classes.…”

Section: Health Data Analysis Phasementioning

confidence: 99%

Vehicular Network based Emergency Data Transmission and Classification for Health Care System using Support Vector Machine

Senapati

Khilar

Dash

et al. 2022

Preprint

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COVID-19 pandemic has created an emergency across the globe. The number of corona positive cases and death cases are still rising across the world. The government of all countries is taking various steps to control the infection of COVID-19. One of the steps to control the spreading of the coronavirus is to quarantine. But the number of active cases at the quarantine center is increasing day by day. Also, the doctors, nurses, and paramedical staff providing service to the people at the quarantine center are getting infected. This demands the automatic and regular monitoring of people at the quarantine center. This paper proposed a novel and automated method for the monitoring of people at the quarantine center by two phases. These are the health data transmission phase and health data analysis phase. The health data transmission phase involves component NIBs, RSUs, and vehicles. For the transmission of data from the quarantine center to the observation center, an effective route is determined using route value. The route-value is dependent on factors like density, shortest path, delay, vehicular data carrying delay, and attenuation. The analysis of health data is done by using multi-class SVM. Finally, simulation results are obtained for the two phases. The simulation results obtained for the health data transmission phase is E2E delay, number of network gaps, and packet delivery ratio. The health data analysis phase determines parameters such as precision, recall, accuracy, etc.

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“…This is a research hotspot of ECOC applications 14,15 . Zhou et al proposed 12 a method derived from an idea already hinted 16 . In Zhou's approach, called the CMSECOC (Confusion Matrix Superclass ECOC), superclasses are formed so that two superclasses are easily separated.…”

Section: Ecoc Strategiesmentioning

confidence: 99%

“…In this study, we investigate more sophisticated binary classifier strategies, e.g. data driven strategies 12 , random selection 7 , and expert knowledge-based strategies. If these approaches have been evaluated in the framework of classic loss-functions, they have never been tested in the framework of belief function theory (BFT).…”

Section: Support Vector Machines (Svms) Introduced By Vapnik and Cortesmentioning

confidence: 99%

Evidential multi-class classification from binary classifiers: application to waste sorting quality control from hyperspectral data

et al. 2017

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Our application deals with waste sorting using an automatic system involving a hyperspectral camera. This latter provides the data for classification of the different kinds of waste allowing the evaluation of mechanical pre-sorting and its refinement. Hyperspectral data are processed using Support Vector Machine (SVM) binary classifiers that we propose to combine in the belief function theory (BFT) framework to take into account not only the performance of each binary classifier, but also its imprecision related for instance to the number of samples during the learning step. Having underlined the interest of BFT framework to deal with sparse classifiers, we study the performance of different combinations of classifiers.

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Constructing ECOC based on confusion matrix for multiclass learning problems

Cited by 13 publications

References 11 publications

Forecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection

Forecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection

Vehicular Network based Emergency Data Transmission and Classification for Health Care System using Support Vector Machine

Evidential multi-class classification from binary classifiers: application to waste sorting quality control from hyperspectral data

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