A Modified Adaptive Synthetic SMOTE Approach in Graduation Success Rate Classification

Gameng, Hazel A.

doi:10.30534/ijatcse/2019/63862019

Cited by 3 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We utilized an embedding dimension of 300 for each language subtask to minimize losses and get the most accurate results and applied the “Adam” optimizer. The dataset for subtask B was so imbalanced that we used the “ADASYN” oversampling technique to balance the data [ 16 ]. Internal activation is based on “ReLU,” and the final output dense layer is on the sigmoid [ 17 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

Analysing Hate Speech against Migrants and Women through Tweets Using Ensembled Deep Learning Model

Hasan

Sharma²,

Khan

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Twitter’s popularity has exploded in the previous few years, making it one of the most widely used social media sites. As a result of this development, the strategies described in this study are now more beneficial. Additionally, there has been an increase in the number of people who express their views in demeaning ways to others. As a result, hate speech has piqued interest in the subject of sentiment analysis, which has developed various algorithms for detecting emotions in social networks using intuitive means. This paper proposes the deep learning model to classify the sentiments in two separate analyses. In the first analysis, the tweets are classified based on the hate speech against the migrants and the women. In the second analysis, the detection is performed using a deep learning model to organise whether the hate speech is performed by a single or a group of users. During the text analysis, word embedding is implemented using the combination of deep learning models such as BiLSTM, CNN, and MLP. These models are integrated with word embedding methods such as inverse glove (global vector), document frequency (TF-IDF), and transformer-based embedding.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

Analysing Hate Speech against Migrants and Women through Tweets Using Ensembled Deep Learning Model

Hasan

Sharma²,

Khan

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…However, this is not the real scenario in the field of education. The imbalanced occurs when a class is underrepresented compared to other classes in educational data [11]. The classifier could be biased to the majority class; thus, handling the imbalance is done.…”

Section: Imbalanced Datasets In Educational Data Mining (Edm)mentioning

confidence: 99%

Affinity Propagation SMOTE approach for Imbalanced dataset used in Predicting Student at Risk of Low Performance

Lanie¹

2020

IJATCSE

View full text Add to dashboard Cite

Imbalanced datasets affect the performance of classification algorithms in predicting student performance. There are several techniques in combatting class imbalance and one of the most known is the Synthetic Minority Oversampling Technique (SMOTE). It is an oversampling technique that generates synthetic data along the line of the minority instances and its neighbors. However, it has a drawback on the distribution and generation of noisy samples which is the main reason of its many variations. In the cluster approaches for SMOTE, Affinity Propagation (AP) SMOTE is one of them. This approach uses affinity propagation to automatically produce clusters and cluster exemplars used to select the clusters to be oversampled. This way, the sparsity and generation of noisy samples will be avoided. The data used for the study is the student performance of freshman students of Davao Oriental State College of Science and Technology (DOSCST) as well as their enrolment data. The dataset comprises 10 features and 2112 instances, the imbalance ratio between majority and minority is 17.85. SMOTE and AP SMOTE are applied to the imbalanced dataset. The output is used in the J48 and Naïve Bayes classifiers to predict the student at risk of getting low performance in their freshman years in the college. The classifiers' performance is evaluated using f-measure, g-mean, and Areas Under the Curve (AUC). Results showed that AP SMOTE outperforms the original SMOTE with a percentage lead of .60%, .88%, 1.2% using the J48 classifier. The percentage lead for Naïve Bayes is 3.2%, 6.58%, 3.30%, respectively.

show abstract

“…In 2019, Gameng et al [5] performed research on a modified adaptive synthetic SMOTE for imbalanced data sets. The primary data set in this research is from an open admission program of a state college and random forest classifier applied with SMOTE and modifier ADASYN.…”

Section: Introductionmentioning

confidence: 99%

Handling Imbalanced Data Sets Using SMOTE and ADASYN to Improve Classification Performance of Ecoli Data Sets

Halim

Dwifebri

Nhita

2023

bits

View full text Add to dashboard Cite

In this digital era, machine learning is a technology that is in demand by organizations and individuals. In the age of data and digital information, the ability to process data efficiently is needed. As the amount of data grows, there are various problems in machine learning. One of them is that with the increasing amount of data, class imbalance is also often found. Class imbalance is a condition where a class dominates another class, in one example case is when the positive value class has less number than the negative class. The class that is less in number is categorized as the minority class, while the class that dominates the dataset is called the majority class. Class imbalance can affect classification performance in a bad way, so handling imbalanced classes is needed to improve classification results. Classification of imbalanced data using Random Forest has satisfactory results, as well as by implementing SMOTE and ADASYN as sampling methods because they are highly popular and easy to implement. The best model produced in this study is the model that applies SMOTE oversampling on a dataset with 10% IR with a balanced accuracy of 98.75%, and the best result when applying ADASYN oversampling is on a dataset with 13% IR and a balanced accuracy of 99.03%.

show abstract

A Modified Adaptive Synthetic SMOTE Approach in Graduation Success Rate Classification

Cited by 3 publications

References 18 publications

Analysing Hate Speech against Migrants and Women through Tweets Using Ensembled Deep Learning Model

Analysing Hate Speech against Migrants and Women through Tweets Using Ensembled Deep Learning Model

Affinity Propagation SMOTE approach for Imbalanced dataset used in Predicting Student at Risk of Low Performance

Handling Imbalanced Data Sets Using SMOTE and ADASYN to Improve Classification Performance of Ecoli Data Sets

Contact Info

Product

Resources

About