PSDC: A Prototype-Based Shared-Dummy Classifier Model for Open-Set Domain Adaptation

Liu, Zhengfa; Chen, Guang; Li, Zhijun; Kang, Yu; Qu, Sanqing; Jiang, Changjun

doi:10.1109/tcyb.2022.3228301

Cited by 14 publications

(5 citation statements)

References 54 publications

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“…It is a simple algorithm that makes predictions by following a pre-defined rule, such as always predicting the most frequent class in the training data or randomly selecting a class based on the class distribution. The purpose of using a dummy classifier is to evaluate whether a more complex classifier can outperform this basic algorithm [50]. Dummy classifiers are useful in situations where the class distribution is imbalanced or when the performance of a classifier needs to be compared against a simple baseline.…”

Section: Dummy Classifiermentioning

confidence: 99%

hyOPTGB: An Efficient OPTUNA Hyperparameter Optimization Framework for Hepatitis C Virus (HCV) Disease Prediction in Egypt

Elshewey

2023

Preprint

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The paper focuses on Hepatitis C Virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence is linked to several factors, including the use of injection drugs, poor sterilization practices in medical facilities, and low public awareness. This paper introduces a model called hyOPTGB, which employs an optimized gradient boosting (GB) classifier to predict HCV disease in Egypt. The model's accuracy is enhanced by optimizing hyperparameters with the OPTUNA framework. Min-Max normalization is used as preprocessing step for scaling the dataset values and also, using the forward selection (FS) wrapped method to identify important features in the dataset. The dataset used in the study contains 1385 instances and 29 features and is available at the UCI machine learning repository. The authors compare the performance of five machine learning models, including decision tree (DT), support vector machine (SVM), dummy classifier (DC), ridge classifier (RC), and bagging classifier (BC), with the hyOPTGB model. The system's efficacy is assessed using various metrics, including accuracy, recall, precision, and F1-score. The hyOPTGB model outperformed the other machine learning models, achieving a 95.3% accuracy rate. The authors also conducted a comparative study of the hyOPTGB model against other models proposed by different authors who used the same dataset.

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Section: Dummy Classifiermentioning

confidence: 99%

hyOPTGB: An Efficient OPTUNA Hyperparameter Optimization Framework for Hepatitis C Virus (HCV) Disease Prediction in Egypt

Elshewey

2023

Preprint

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“…To address the challenge of inconsistent label spaces between source and target domains, various studies have introduced methods tailored to specific scenarios, such as partial-set domain adaptation (PDA) [9], [10], [42], open-set domain adaptation (OSDA) [11], [43], [44], [45], [46], and open-partialset domain adaptation (OPDA) [13], [14], [47], [19]. While these approaches constitute notable progress in addressing discrepancies in label spaces, their applicability is often confined to the scenarios for which they were specifically designed.…”

Section: Universal Domain Adaptationmentioning

confidence: 99%

Upcycling Models Under Domain and Category Shift

Qu,

Zou,

Röhrbein

et al. 2023

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Self Cite

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Deep neural networks often exhibit sub-optimal performance under covariate and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma, yet most SFDA approaches are restricted to closed-set scenarios. In this paper, we explore Source-Free Universal Domain Adaptation (SF-UniDA) aiming to accurately classify "known" data belonging to common categories and segregate them from target-private "unknown" data. We propose a novel Global and Local Clustering (GLC) technique, which comprises an adaptive one-vs-all global clustering algorithm to discern between target classes, complemented by a local k-NN clustering strategy to mitigate negative transfer. Despite the effectiveness, the inherent closed-set source architecture leads to uniform treatment of "unknown" data, impeding the identification of distinct "unknown" categories. To address this, we evolve GLC to GLC++, integrating a contrastive affinity learning strategy. We examine the superiority of GLC and GLC++ across multiple benchmarks and category shift scenarios. Remarkably, in the most challenging open-partial-set scenarios, GLC and GLC++ surpass GATE by 16.7% and 18.6% in H-score on VisDA, respectively. GLC++ enhances the novel category clustering accuracy of GLC by 4.3% in open-set scenarios on Office-Home. Furthermore, the introduced contrastive learning strategy not only enhances GLC but also significantly facilitates existing methodologies.

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“…It is a simple algorithm that makes predictions by following a pre-defined rule, such as always predicting the most frequent class in the training data or randomly selecting a class based on the class distribution. A dummy classifier is used to evaluate whether a more complex classifier can outperform this basic algorithm [56]. Dummy classifiers are helpful in situations where the class distribution is imbalanced or when the performance of a classifier needs to be compared against a simple baseline.…”

Section: Dummy Classifiermentioning

confidence: 99%

Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework

Elshewey,

Shams,

Tawfeek

et al. 2023

Diagnostics

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The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence is linked to several factors, including the use of injection drugs, poor sterilization practices in medical facilities, and low public awareness. This paper introduces a hyOPTGB model, which employs an optimized gradient boosting (GB) classifier to predict HCV disease in Egypt. The model’s accuracy is enhanced by optimizing hyperparameters with the OPTUNA framework. Min-Max normalization is used as a preprocessing step for scaling the dataset values and using the forward selection (FS) wrapped method to identify essential features. The dataset used in the study contains 1385 instances and 29 features and is available at the UCI machine learning repository. The authors compare the performance of five machine learning models, including decision tree (DT), support vector machine (SVM), dummy classifier (DC), ridge classifier (RC), and bagging classifier (BC), with the hyOPTGB model. The system’s efficacy is assessed using various metrics, including accuracy, recall, precision, and F1-score. The hyOPTGB model outperformed the other machine learning models, achieving a 95.3% accuracy rate. The authors also compared the hyOPTGB model against other models proposed by authors who used the same dataset.

show abstract

PSDC: A Prototype-Based Shared-Dummy Classifier Model for Open-Set Domain Adaptation

Cited by 14 publications

References 54 publications

hyOPTGB: An Efficient OPTUNA Hyperparameter Optimization Framework for Hepatitis C Virus (HCV) Disease Prediction in Egypt

hyOPTGB: An Efficient OPTUNA Hyperparameter Optimization Framework for Hepatitis C Virus (HCV) Disease Prediction in Egypt

Upcycling Models Under Domain and Category Shift

Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework

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