Improving meta-learning model via meta-contrastive loss

2022 ◽  
Vol 16 (5) ◽  
Author(s):  
Pinzhuo Tian ◽  
Yang Gao
Keyword(s):  
Learning Model ◽  
Meta Learning

Hypervolemia Screening for Dialysis Patient Healthcare Using Meta Learning Model-Based Intelligent Scaler

Smart Science ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 16-27
Author(s):  
Wei-Ling Chen ◽  
Hsiang-Yueh Lai ◽  
Pi-Yun Chen ◽  
Chung-Dann Kan ◽  
Chia-Hung Lin
Keyword(s):  
Dialysis Patient ◽  
Learning Model ◽  
Model Based ◽  
Meta Learning

scholarly journals A Joint Optimization Framework of the Embedding Model and Classifier for Meta-Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhongyu Liu ◽  
Xu Chu ◽  
Yan Lu ◽  
Wanli Yu ◽  
Shuguang Miao ◽  
...  
Keyword(s):  
Texture Features ◽  
Current Method ◽  
Optimization Method ◽  
Learning Model ◽  
Feature Representation ◽  
Joint Optimization ◽  
Small Samples ◽  
Semantic Features ◽  
Quantum Genetic Algorithm ◽  
Meta Learning

The aim of meta-learning is to train the machine to learn quickly and accurately. Improving the performance of the meta-learning model is important in solving the problem of small samples and in achieving general artificial intelligence. A meta-learning method based on feature embedding that exhibits good performance on the few-shot problem was previously proposed. In this method, the pretrained deep convolution neural network was used as the embedding model of sample features, and the output of one layer was used as the feature representation of samples. The main limitation of the method is the inability to fuse low-level texture features and high-level semantic features of the embedding model and joint optimization of the embedding model and classifier. Therefore, a multilayer adaptive joint training and optimization method of the embedding model was proposed in the current study. The main characteristics of the current method include using multilayer adaptive hierarchical loss to train the embedding model and using the quantum genetic algorithm to jointly optimize the embedding model and classifier. Validation was performed based on multiple public datasets for meta-learning model testing. The proposed method shows higher accuracy compared with multiple baseline methods.

Biodanza in the Development of High- Performance Teams: Embodying the Appreciative Paradigm

2021 ◽  
Vol 23 (3) ◽  
pp. 16-31
Author(s):  
Felipe Losada Carrasco ◽  
Gittith Sánchez Padilla
Keyword(s):  
High Performance ◽  
Learning Model ◽  
The Body ◽  
Integration System ◽  
Form Part ◽  
Meta Learning ◽  
Performance Teams

Cada día sentimos en nuestro cuerpo sensaciones agradables o desagradables, agitación o calma, influenciando la conexión con las demás personas que forman parte del equipo. La práctica del sistema de integración humana “Biodanza” aumenta la conexión del equipo, lo que se refleja en el aumento de positividad y la reducción de la negatividad de los afectos medidos a través del test PANAS, indicadores que caracterizan a los equipos de alto desempeño según el modelo Meta Learning. El cuerpo y las experiencias integradoras son los protagonistas de este proceso. Every day in our bodies we feel pleasant or unpleasant sensations, agitation, or calm, which influence the connections we have with other people who form part of a team. Practising the ‘Biodanza’ human integration system increases the connections within a team, which is reflected in increased positivity and reduced negativity of affect as measured with the PANAS test, indicators that characterise high-performance teams according to the Meta Learning model. The body and the integrative experiences are the protagonists in this process.

Enhanced Data Stream Classification by Optimized Weight Updated Meta-learning: Continuous learning-based on Concept-Drift

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maisnam Niranjan Singh ◽  
Samitha Khaiyum
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Concept Drift ◽  
Learning Model ◽  
Streaming Data ◽  
Spotted Hyena ◽  
Continuous Learning ◽  
Content Type ◽  
Data Stream Classification ◽  
Meta Learning

Purpose The aim of continuous learning is to obtain and fine-tune information gradually without removing the already existing information. Many conventional approaches in streaming data classification assume that all arrived new data is completely labeled. To regularize Neural Networks (NNs) by merging side information like user-provided labels or pair-wise constraints, incremental semi-supervised learning models need to be introduced. However, they are hard to implement, specifically in non-stationary environments because of the efficiency and sensitivity of such algorithms to parameters. The periodic update and maintenance of the decision method is the significant challenge in incremental algorithms whenever the new data arrives. Design/methodology/approach Hence, this paper plans to develop the meta-learning model for handling continuous or streaming data. Initially, the data pertain to continuous behavior is gathered from diverse benchmark source. Further, the classification of the data is performed by the Recurrent Neural Network (RNN), in which testing weight is adjusted or optimized by the new meta-heuristic algorithm. Here, the weight is updated for reducing the error difference between the target and the measured data when new data is given for testing. The optimized weight updated testing is performed by evaluating the concept-drift and classification accuracy. The new continuous learning by RNN is accomplished by the improved Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO). Finally, the experiments with different datasets show that the proposed learning is improved over the conventional models. Findings From the analysis, the accuracy of the ONU-SHO based RNN (ONU-SHO-RNN) was 10.1% advanced than Decision Tree (DT), 7.6% advanced than Naive Bayes (NB), 7.4% advanced than k-nearest neighbors (KNN), 2.5% advanced than Support Vector Machine (SVM) 9.3% advanced than NN, and 10.6% advanced than RNN. Hence, it is confirmed that the ONU-SHO algorithm is performing well for acquiring the best data stream classification. Originality/value This paper introduces a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data. This is the first work utilizes a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data.

scholarly journals Deep Learning Generates Custom-made Logistic Regression Models for Explaining how Breast Cancer Subtypes are Classified

2021 ◽  
Author(s):  
Takuma Shibahara ◽  
Chisa Wada ◽  
Yasuho Yamashita ◽  
Kazuhiro Fujita ◽  
Masamichi Sato ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Deep Learning ◽  
Linear Model ◽  
Regression Models ◽  
Learning Model ◽  
Logistic Regression Models ◽  
Custom Made ◽  
Meta Learning ◽  
Deep Learning Model

Abstract Breast cancer is the most frequently found cancer in women and the one most often subjected to genetic analysis. Nonetheless, it has been causing the largest number of women's cancer-related deaths. PAM50, the intrinsic subtype assay for breast cancer, is beneficial for diagnosis but does not explain each subtype’s mechanism. Deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods. However, the previous studies did not directly use deep learning to examine which genes associate with the subtypes. To reveal the mechanisms embedded in the PAM50 subtypes, we developed an explainable deep learning model called a point-wise linear model, which uses meta-learning to generate a custom-made logistic regression for each sample. We developed an explainable deep learning model called a point-wise linear model, which uses meta-learning to generate a custom-made logistic regression for each sample. Logistic regression is familiar to physicians, and we can use it to analyze which genes are important for prediction. The custom-made logistic regression models generated by the point-wise linear model used the specific genes selected in other subtypes compared to the conventional logistic regression model: the overlap ratio is less than twenty percent. Analyzing the point-wise linear model’s inner state, we found that the point-wise linear model used genes relevant to the cell cycle-related pathways.

scholarly journals UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rui Miao ◽  
Xin Dong ◽  
Sheng-Li Xie ◽  
Yong Liang ◽  
Sio-Long Lo
Keyword(s):  
Recognition Task ◽  
Training Model ◽  
Learning Model ◽  
Model Parameters ◽  
X Ray ◽  
Learning Framework ◽  
Fast Screening ◽  
Rapid Spread ◽  
Meta Learning ◽  
Multi Class Classification

Abstract Background With the rapid spread of COVID-19 worldwide, quick screening for possible COVID-19 patients has become the focus of international researchers. Recently, many deep learning-based Computed Tomography (CT) image/X-ray image fast screening models for potential COVID-19 patients have been proposed. However, the existing models still have two main problems. First, most of the existing supervised models are based on pre-trained model parameters. The pre-training model needs to be constructed on a dataset with features similar to those in COVID-19 X-ray images, which limits the construction and use of the model. Second, the number of categories based on the X-ray dataset of COVID-19 and other pneumonia patients is usually imbalanced. In addition, the quality is difficult to distinguish, leading to non-ideal results with the existing model in the multi-class classification COVID-19 recognition task. Moreover, no researchers have proposed a COVID-19 X-ray image learning model based on unsupervised meta-learning. Methods This paper first constructed an unsupervised meta-learning model for fast screening of COVID-19 patients (UMLF-COVID). This model does not require a pre-trained model, which solves the limitation problem of model construction, and the proposed unsupervised meta-learning framework solves the problem of sample imbalance and sample quality. Results The UMLF-COVID model is tested on two real datasets, each of which builds a three-category and four-category model. And the experimental results show that the accuracy of the UMLF-COVID model is 3–10% higher than that of the existing models. Conclusion In summary, we believe that the UMLF-COVID model is a good complement to COVID-19 X-ray fast screening models.

scholarly journals Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space

2020 ◽  
Vol 12 (7) ◽  
pp. 1095 ◽  
Author(s):  
Ruhollah Taghizadeh-Mehrjardi ◽  
Karsten Schmidt ◽  
Alireza Amirian-Chakan ◽  
Tobias Rentschler ◽  
Mojtaba Zeraatpisheh ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Accuracy ◽  
Learning Model ◽  
Weighted Averaging ◽  
Climatic Regions ◽  
Meta Learning

Understanding the spatial distribution of soil organic carbon (SOC) content over different climatic regions will enhance our knowledge of carbon gains and losses due to climatic change. However, little is known about the SOC content in the contrasting arid and sub-humid regions of Iran, whose complex SOC–landscape relationships pose a challenge to spatial analysis. Machine learning (ML) models with a digital soil mapping framework can solve such complex relationships. Current research focusses on ensemble ML models to increase the accuracy of prediction. The usual ensemble method is boosting or weighted averaging. This study proposes a novel ensemble technique: the stacking of multiple ML models through a meta-learning model. In addition, we tested the ensemble through rescanning the covariate space to maximize the prediction accuracy. We first applied six state-of-the-art ML models (i.e., Cubist, random forests (RF), extreme gradient boosting (XGBoost), classical artificial neural network models (ANN), neural network ensemble based on model averaging (AvNNet), and deep learning neural networks (DNN)) to predict and map the spatial distribution of SOC content at six soil depth intervals for both regions. In addition, the stacking of multiple ML models through a meta-learning model with/without rescanning the covariate space were tested and applied to maximize the prediction accuracy. Out of six ML models, the DNN resulted in the best modeling accuracies, followed by RF, XGBoost, AvNNet, ANN, and Cubist. Importantly, the stacking of models indicated a significant improvement in the prediction of SOC content, especially when combined with rescanning the covariate space. For instance, the RMSE values for SOC content prediction of the upper 0–5 cm of the soil profiles of the arid site and the sub-humid site by the proposed stacking approaches were 17% and 9% respectively, less than that obtained by the DNN models—the best individual model. This indicates that rescanning the original covariate space by a meta-learning model can extract more information and improve the SOC content prediction accuracy. Overall, our results suggest that the stacking of diverse sets of models could be used to more accurately estimate the spatial distribution of SOC content in different climatic regions.

scholarly journals Metric-based meta-learning model for few-shot fault diagnosis under multiple limited data conditions

2021 ◽  
Vol 155 ◽  
pp. 107510
Author(s):  
Duo Wang ◽  
Ming Zhang ◽  
Yuchun Xu ◽  
Weining Lu ◽  
Jun Yang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Learning Model ◽  
Limited Data ◽  
Meta Learning
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