Enhancing Multi-Class Classification in One-Versus-One Strategy: A Type of Base Classifier Modification and Weighted Voting Mechanism

2021 ◽  
Author(s):  
Yanghao Xiao ◽  
Yucheng Liu ◽  
Yuanyuan Deng ◽  
Haoxuan Li
Keyword(s):  
Weighted Voting ◽  
Base Classifier ◽  
Multi Class Classification ◽  
Voting Mechanism

The Semantic Label Automatic Sorting Based on the Base Classifier Weighted Voting

2014 ◽  
Vol 530-531 ◽  
pp. 476-479
Author(s):  
Yong Wang
Keyword(s):  
Weighted Voting ◽  
Current Image ◽  
Base Classifier ◽  
Automatic Sorting ◽  
Automatic Tagging ◽  
Semantic Label ◽  
Reference Index ◽  
The Right ◽  
Right Order

The ranking results of semantic label are the important reference index on the point whether the image sematic automatic tagging results are right. To the problem of the disorder of the current image sematic labels, this paper suggests a method of the image semantic label automatic sorting which is based on the base classifier weighted voting. This method based on the content in the significant image regional, it weighted votes on every part of the semantic label, with the help of the base classifier. In this way, it tests the relevance of each semantic label and the image that makes the right order of the image label.

scholarly journals A Weighted Voting Classifier Based on Differential Evolution

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Yong Zhang ◽  
Hongrui Zhang ◽  
Jing Cai ◽  
Binbin Yang
Keyword(s):  
Differential Evolution ◽  
Ensemble Learning ◽  
Classification Accuracy ◽  
Experimental Results ◽  
Combination Rule ◽  
Weighted Voting ◽  
Classification Result ◽  
Generalization Ability ◽  
Base Classifier

Ensemble learning is to employ multiple individual classifiers and combine their predictions, which could achieve better performance than a single classifier. Considering that different base classifier gives different contribution to the final classification result, this paper assigns greater weights to the classifiers with better performance and proposes a weighted voting approach based on differential evolution. After optimizing the weights of the base classifiers by differential evolution, the proposed method combines the results of each classifier according to the weighted voting combination rule. Experimental results show that the proposed method not only improves the classification accuracy, but also has a strong generalization ability and universality.

scholarly journals Cost-sensitive probability for weighted voting in an ensemble model for multi-class classification problems

2021 ◽  
Author(s):  
Artittayapron Rojarath ◽  
Wararat Songpan
Keyword(s):  
Ensemble Learning ◽  
Support Vector ◽  
Ensemble Model ◽  
Classification Models ◽  
Classification Problems ◽  
Sensitivity Matrix ◽  
Weighted Voting ◽  
Class Prediction ◽  
Multi Class Classification ◽  
The Individual

AbstractEnsemble learning is an algorithm that utilizes various types of classification models. This algorithm can enhance the prediction efficiency of component models. However, the efficiency of combining models typically depends on the diversity and accuracy of the predicted results of ensemble models. However, the problem of multi-class data is still encountered. In the proposed approach, cost-sensitive learning was implemented to evaluate the prediction accuracy for each class, which was used to construct a cost-sensitivity matrix of the true positive (TP) rate. This TP rate can be used as a weight value and combined with a probability value to drive ensemble learning for a specified class. We proposed an ensemble model, which was a type of heterogenous model, namely, a combination of various individual classification models (support vector machine, Bayes, K-nearest neighbour, naïve Bayes, decision tree, and multi-layer perceptron) in experiments on 3-, 4-, 5- and 6-classifier models. The efficiencies of the propose models were compared to those of the individual classifier model and homogenous models (Adaboost, bagging, stacking, voting, random forest, and random subspaces) with various multi-class data sets. The experimental results demonstrate that the cost-sensitive probability for the weighted voting ensemble model that was derived from 3 models provided the most accurate results for the dataset in multi-class prediction. The objective of this study was to increase the efficiency of predicting classification results in multi-class classification tasks and to improve the classification results.

Random Subspace Based ECOC Classifier with Reject Option

2013 ◽  
Vol 846-847 ◽  
pp. 1282-1285
Author(s):  
Hao Pan ◽  
Bai Ling Zhang
Keyword(s):  
Classification Accuracy ◽  
Decision Fusion ◽  
Vehicle Classification ◽  
Random Subspace ◽  
Subspace Method ◽  
Reject Option ◽  
Random Subspace Method ◽  
Base Classifier ◽  
Multi Class Classification ◽  
Classification Reliability

ECOC based multi-class classification has been a topic of research interests for many years. Yet most of the previous studies concentrated only on different coding and decoding strategies aiming at improvement over classification accuracies. In this paper, the classification reliability is addressed. By applying the Random Subspace method, a base classifier is created for each of the coding position. The improvement over classification accuracy on each of the coding position is achieved by a reject option and decision fusion. By rejection of those low-confidence samples, the systems reliability is enhanced. The performance of the proposed system was demonstrated by a vehicle classification example, showing promising results.

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

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