An Enhanced Optimization Scheme Based on Gradient Descent Methods for Machine Learning

Yi, Dokkyun; Ji, Sangmin; Bu, Sunyoung

doi:10.3390/sym11070942

Cited by 15 publications

(9 citation statements)

References 16 publications

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“…From this figure, it can be concluded that gradient boost algorithm gives the best F1-score [27] of around 0.7 and k-neighbors classifiers gives the lowest F1-score of 0.6 among these four algorithms, in addition to this logistic regression and support vector classifier gives same F1-score of 0.66. From the simulation results, it can be concluded that in this paper there are total 4 algorithms are used LR [28], KNN [29], gradient descent [30], and the best accuracy achieved is 81.25% which has given by gradient descent classifier.…”

Section: Resultsmentioning

confidence: 99%

Prediction of diabetes disease using machine learning algorithms

Panda

Mishra

Patro

et al. 2022

IJ-AI

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Diabetes mellitus is a powerful chronic disease, which is recognized by lack of capability of our body for metabolization of glucose. Diabetes is one of the most dangerous diseases and a threat to human society, many are becoming its victims and, regardless of the fact that they are trying to keep it from rising more, are unable to come out of it. There are several conventional diabetes disease health monitoring strategies. This disease was examined by machine learning (ML) algorithms in this paper. The goal behind this research is to create an effective model with high precision to predict diabetes. In order to reduce the processing time, K-nearest neighbor algorithm is used. In addition, support vector machine is also introduced to allocate its respective class to each and every sample of data. In building any sort of ML model, feature selection plays a vital role, it is the process where we select the features automatically or manually and it contributes most to our desired performance. Overall, four algorithms are used in this paper to understand which can easily evaluate the total effectiveness and accuracy of predicting whether or not a person will suffer from diabetes.

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Section: Resultsmentioning

confidence: 99%

Prediction of diabetes disease using machine learning algorithms

Panda

Mishra

Patro

et al. 2022

IJ-AI

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“…Equation (11) presents the Logistic Regression probability. (11) To estimate the classes ŷi = argmax yκ∈C p(y = y κ |x = x i ) and to determine the parameters w that give the best results, the algorithm maximises the log likelihood using gradient descent [49]. The log likelihood function guarantees that the gradient descent algorithm can converge to the global minimum.…”

Section: Machine and Deep Learning Architectures 1) Logistic Regressionmentioning

confidence: 99%

Context-Aware Misinformation Detection: A Benchmark of Deep Learning Architectures Using Word Embeddings

Ilie

Truică²,

Apostol

et al. 2021

IEEE Access

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New mass media paradigms for information distribution have emerged with the digital age. With new digital-enabled mass media, the communication process is centered around the user, while multimedia content is the new identity of news. Thus, the media landscape has shifted from mass media to personalized social media. While this progress brings advantages, it also carries the risk of being detrimental to society through the emergence of misinformation (false or inaccurate information) and disinformation (intentionally spreading misinformation) in the form of fake news. Fake news is a tool used to manipulate public opinion on particular topics, distort public perceptions, and generate social unrest while lacking the rigor of traditional journalism. Driven by this current and real-world problem, in this paper, we train multiple Deep Learning architectures for multi-class classification and compare their performance in detecting the veracity of the news articles. To achieve accurate models in detecting misinformation, we employ a large dataset containing 100 000 news articles labeled with ten classes (one with real news and the rest with different types of fake news). We use two preprocessing techniques, i.e., one simple and another very aggressive, to clean the dataset. We also employ three word embeddings that preserve the word context, i.e., Word2Vec, FastText, and GloVe, pre-trained and trained on our dataset to vectorize the preprocessed dataset. For the misinformation task, we train a Logistic Regression as a baseline and compare its results with the performance of ten Deep Learning architectures. We obtain the best results using a Recurrent Convolutional Neural Network based architecture. The experimental results show that the models are highly dependable on text preprocessing and the word embedding employed.

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“…Logistic Regression (LOGREG) is a classification algorithm successfully used, in many cases, as a baseline for the Sentiment Analysis task to predict the class in which an observation can be categorized [36,37]. The algorithm tries to minimize the error of the estimations made using the log-likelihood and to determine the parameters that produce the best estimations using gradient descent [38]. The log-likelihood functions guarantee that the gradient descent algorithm can converge to the global minimum.…”

Section: Logistic Regressionmentioning

confidence: 99%

Topic-Based Document-Level Sentiment Analysis Using Contextual Cues

et al. 2021

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Document-level Sentiment Analysis is a complex task that implies the analysis of large textual content that can incorporate multiple contradictory polarities at the phrase and word levels. Most of the current approaches either represent textual data using pre-trained word embeddings without considering the local context that can be extracted from the dataset, or they detect the overall topic polarity without considering both the local and global context. In this paper, we propose a novel document-topic embedding model, DocTopic2Vec, for document-level polarity detection in large texts by employing general and specific contextual cues obtained through the use of document embeddings (Doc2Vec) and Topic Modeling. In our approach, (1) we use a large dataset with game reviews to create different word embeddings by applying Word2Vec, FastText, and GloVe, (2) we create Doc2Vecs enriched with the local context given by the word embeddings for each review, (3) we construct topic embeddings Topic2Vec using three Topic Modeling algorithms, i.e., LDA, NMF, and LSI, to enhance the global context of the Sentiment Analysis task, (4) for each document and its dominant topic, we build the new DocTopic2Vec by concatenating the Doc2Vec with the Topic2Vec created with the same word embedding. We also design six new Convolutional-based (Bidirectional) Recurrent Deep Neural Network Architectures that show promising results for this task. The proposed DocTopic2Vecs are used to benchmark multiple Machine and Deep Learning models, i.e., a Logistic Regression model, used as a baseline, and 18 Deep Neural Networks Architectures. The experimental results show that the new embedding and the new Deep Neural Network Architectures achieve better results than the baseline, i.e., Logistic Regression and Doc2Vec.

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An Enhanced Optimization Scheme Based on Gradient Descent Methods for Machine Learning

Cited by 15 publications

References 16 publications

Prediction of diabetes disease using machine learning algorithms

Prediction of diabetes disease using machine learning algorithms

Context-Aware Misinformation Detection: A Benchmark of Deep Learning Architectures Using Word Embeddings

Topic-Based Document-Level Sentiment Analysis Using Contextual Cues

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