Meta-Learner for Amharic Sentiment Classification

Neshir, Girma; Rauber, Andreas; Atnafu, Solomon

doi:10.3390/app11188489

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…Deep ensemble learning models combine the advantages of deep learning and ensemble learning to improve the generalization performance of the model. In this regard, several researchers have used ensemble learning in their studies [41][42][43][44].…”

Section: Related Workmentioning

confidence: 99%

Ensemble of 2D Residual Neural Networks Integrated with Atrous Spatial Pyramid Pooling Module for Myocardium Segmentation of Left Ventricle Cardiac MRI

et al. 2022

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Cardiac disease diagnosis and identification is problematic mostly by inaccurate segmentation of the cardiac left ventricle (LV). Besides, LV segmentation is challenging since it involves complex and variable cardiac structures in terms of components and the intricacy of time-based crescendos. In addition, full segmentation and quantification of the LV myocardium border is even more challenging because of different shapes and sizes of the myocardium border zone. The foremost purpose of this research is to design a precise automatic segmentation technique employing deep learning models for the myocardium border using cardiac magnetic resonance imaging (MRI). The ASPP module (Atrous Spatial Pyramid Pooling) was integrated with a proposed 2D-residual neural network for segmentation of the myocardium border using a cardiac MRI dataset. Further, the ensemble technique based on a majority voting ensemble method was used to blend the results of recent deep learning models on different set of hyperparameters. The proposed model produced an 85.43% dice score on validation samples and 98.23% on training samples and provided excellent performance compared to recent deep learning models. The myocardium border was successfully segmented across diverse subject slices with different shapes, sizes and contrast using the proposed deep learning ensemble models. The proposed model can be employed for automatic detection and segmentation of the myocardium border for precise quantification of reflow, myocardial infarction, myocarditis, and h cardiomyopathy (HCM) for clinical applications.

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Section: Related Workmentioning

confidence: 99%

Ensemble of 2D Residual Neural Networks Integrated with Atrous Spatial Pyramid Pooling Module for Myocardium Segmentation of Left Ventricle Cardiac MRI

et al. 2022

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“…Using three publicly available datasets, SMOTE has shown performance gains in tweet polarity classification [26]. Similarly, Neshir et al [27] have shown that SMOTE is improving the performance of sentiment classification using four datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

Topic Modeling for Amharic User Generated Texts

2021

Self Cite

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Topic Modeling is a statistical process, which derives the latent themes from extensive collections of text. Three approaches to topic modeling exist, namely, unsupervised, semi-supervised and supervised. In this work, we develop a supervised topic model for an Amharic corpus. We also investigate the effect of stemming on topic detection on Term Frequency Inverse Document Frequency (TF-IDF) features, Latent Dirichlet Allocation (LDA) features and a combination of these two feature sets using four supervised machine learning tools, that is, Support Vector Machine (SVM), Naive Bayesian (NB), Logistic Regression (LR), and Neural Nets (NN). We evaluate our approach using an Amharic corpus of 14,751 documents of ten topic categories. Both qualitative and quantitative analysis of results show that our proposed supervised topic detection outperforms with an accuracy of 88% by SVM using state-of-the-art-approach TF-IDF word features with the application of the Synthetic Minority Over-sampling Technique (SMOTE) and with no stemming operation. The results show that text features with stemming slightly improve the performance of the topic classifier over features with no stemming.

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“…Currently, many studies have attempted to address the issue of biased data, mainly employing two methods: sample re-sampling and sample reweighting. Sample re-sampling includes over-sampling [9], under-sampling, and data augmentation. Sample reweighting, on the other hand, assigns weights to each sample based on a weighted function, adjusting the impact of each sample on the model through weighted losses.…”

Section: Introductionmentioning

confidence: 99%

MLRNet: A Meta-Loss Reweighting Network for Biased Data on Text Classification

Yu,

2023

Applied Sciences

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Artificially generated datasets often exhibit biases, leading conventional deep neural networks to overfit. Typically, a weighted function adjusts sample impact during model updates using weighted loss. Meta-neural networks, trained with meta-learning principles, generalize well across tasks, acquiring generalized weights. This enables the self-generation of tailored weighted functions for data biases. However, datasets may simultaneously exhibit imbalanced classes and corrupted labels, posing a challenge for current meta-models. To address this, this paper presents Meta-Loss Reweighting Network (MLRNet) with fusion attention features. MLRNet continually evolves sample loss values, integrating them with sample features from self-attention layers in a semantic space. This enhances discriminative power for biased samples. By employing minimal unbiased meta-data for guidance, mutual optimization between the classifier and the meta-model is conducted, endowing biased samples with more reasonable weights. Experiments on English and Chinese benchmark datasets including artificial and real-world biased data show MLRNet’s superior performance under biased data conditions.

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Meta-Learner for Amharic Sentiment Classification

Cited by 7 publications

References 30 publications

Ensemble of 2D Residual Neural Networks Integrated with Atrous Spatial Pyramid Pooling Module for Myocardium Segmentation of Left Ventricle Cardiac MRI

Ensemble of 2D Residual Neural Networks Integrated with Atrous Spatial Pyramid Pooling Module for Myocardium Segmentation of Left Ventricle Cardiac MRI

Topic Modeling for Amharic User Generated Texts

MLRNet: A Meta-Loss Reweighting Network for Biased Data on Text Classification

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