Linear Support Vector Machine and Logistic Regression for Cerebral Infarction Classification

Sa'id, Alva Andhika; Rustam, Zuherman; Wibowo, Velery Virgina Putri; Setiawan, Qisthina Syifa; Laeli, Afifah Rofi

doi:10.1109/dasa51403.2020.9317065

“…LR is one of the ML classification algorithms for analyzing the dataset in which there are one or more independent variables that identify the outcome and the categorical dependent variable ( Bertram, Lill & Tanzi, 2010 ). In many ways, LR is the natural complement of normal linear regression when the target variable is categorized ( Sa’id et al, 2020 ). For output (dependent) variable Y to classify two class and input (independent) variable X, let g ( x ) = pr ( X = x ) = 1 − pr ( X = x ), the LR model has a linear form for logit probability as follows:…”

Section: Methodsmentioning

confidence: 99%

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Ahmed

¹

,

Alarabi

²

,

El–Sappagh

³

et al. 2021

PeerJ Computer Science

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Background and Objectives This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain’s genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer’s disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges. Methods We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail. Results It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes. Conclusions Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.

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“…L-SVC performed better than LR properly due to L-SVC attempting to exploit the margin between the closest support vectors whereas LR exploits the posterior class probability. 37 From Table 1, there is possible limitation factor cause by the capacity of the training data, therefore the size of the dataset is increased through the data augmentation technique. 38 As LR has a simpler architecture, data augmentation is not considered, and the focus is made on L-SVC and LSTM-ATT.…”

Section: Sentiment Analysismentioning

confidence: 99%

Modelling sentiments based on objectivity and subjectivity with self-attention mechanisms

Ng

¹

,

Chia

²

,

Yap

³

et al. 2022

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Background: The proliferation of digital commerce has allowed merchants to reach out to a wider customer base, prompting a study of customer reviews to gauge service and product quality through sentiment analysis. Sentiment analysis can be enhanced through subjectivity and objectivity classification with attention mechanisms. Methods: This research includes input corpora of contrasting levels of subjectivity and objectivity from different databases to perform sentiment analysis on user reviews, incorporating attention mechanisms at the aspect level. Three large corpora are chosen as the subjectivity and objectivity datasets, the Shopee user review dataset (ShopeeRD) for subjectivity, together with the Wikipedia English dataset (Wiki-en) and Internet Movie Database (IMDb) for objectivity. Word embeddings are created using Word2Vec with Skip-Gram. Then, a bidirectional LSTM with an attention layer (LSTM-ATT) imposed on word vectors. The performance of the model is evaluated and benchmarked against classification models of Logistics Regression (LR) and Linear SVC (L-SVC). Three models are trained with subjectivity (70% of ShopeeRD) and the objectivity (Wiki-en) embeddings, with ten-fold cross-validation. Next, the three models are evaluated against two datasets (IMDb and 20% of ShopeeRD). The experiments are based on benchmark comparisons, embedding comparison and model comparison with 70-10-20 train-validation-test splits. Data augmentation using AUG-BERT is performed and selected models incorporating AUG-BERT, are compared. Results: L-SVC scored the highest accuracy with 56.9% for objective embeddings (Wiki-en) while the LSTM-ATT scored 69.0% on subjective embeddings (ShopeeRD). Improved performances were observed with data augmentation using AUG-BERT, where the LSTM-ATT+AUG-BERT model scored the highest accuracy at 60.0% for objective embeddings and 70.0% for subjective embeddings, compared to 57% (objective) and 69% (subjective) for L-SVC+AUG-BERT, and 56% (objective) and 68% (subjective) for L-SVC. Conclusions: Utilizing attention layers with subjectivity and objectivity notions has shown improvement to the accuracy of sentiment analysis models.

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“…L-SVC performed better than LR properly due to L-SVC attempting to exploit the margin between the closest support vectors whereas LR exploits the posterior class probability. 30 From Table 1, there is possible limitation factor cause by the capacity of the training data, therefore the size of the dataset is increased through the data augmentation technique. 31 As LR has a simpler architecture, data augmentation is not considered, and the focus is made on L-SVC and LSTM-ATT.…”

Section: Sentiment Analysismentioning

confidence: 99%

Modelling sentiments based on objectivity and subjectivity with self-attention mechanisms

Ng

¹

,

Chia

²

,

Yap

³

et al. 2021

F1000Res

0

View full text Add to dashboard Cite

Background: The proliferation of digital commerce has allowed merchants to reach out to a wider customer base, prompting a study of customer reviews to gauge service and product quality through sentiment analysis. Sentiment analysis can be enhanced through subjectivity and objectivity classification with attention mechanisms. Methods: This research includes input corpora of contrasting levels of subjectivity and objectivity from different databases to perform sentiment analysis on user reviews, incorporating attention mechanisms at the aspect level. Three large corpora are chosen as the subjectivity and objectivity datasets, the Shopee user review dataset (ShopeeRD) for subjectivity, together with the Wikipedia English dataset (Wiki-en) and Internet Movie Database (IMDb) for objectivity. Word embeddings are created using Word2Vec with Skip-Gram. Then, a bidirectional LSTM with an attention layer (LSTM-ATT) imposed on word vectors. The performance of the model is evaluated and benchmarked against classification models of Logistics Regression (LR) and Linear SVC (L-SVC). Three models are trained with subjectivity (70% of ShopeeRD) and the objectivity (Wiki-en) embeddings, with ten-fold cross-validation. Next, the three models are evaluated against two datasets (IMDb and 20% of ShopeeRD). The experiments are based on benchmark comparisons, embedding comparison and model comparison with 70-10-20 train-validation-test splits. Data augmentation using AUG-BERT is performed and selected models incorporating AUG-BERT, are compared. Results: L-SVC scored the highest accuracy with 56.9% for objective embeddings (Wiki-en) while the LSTM-ATT scored 69.0% on subjective embeddings (ShopeeRD). Improved performances were observed with data augmentation using AUG-BERT, where the LSTM-ATT+AUG-BERT model scored the highest accuracy at 60.0% for objective embeddings and 70.0% for subjective embeddings, compared to 57% (objective) and 69% (subjective) for L-SVC+AUG-BERT, and 56% (objective) and 68% (subjective) for L-SVC. Conclusions: Utilizing attention layers with subjectivity and objectivity notions has shown improvement to the accuracy of sentiment analysis models.

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Linear Support Vector Machine and Logistic Regression for Cerebral Infarction Classification

Cited by 9 publications

References 12 publications

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Modelling sentiments based on objectivity and subjectivity with self-attention mechanisms

Modelling sentiments based on objectivity and subjectivity with self-attention mechanisms

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