Multi-view Document Classification with Co-training

Sevim, Semih; Ekinci, Ekin; Omurca, Sevinç İlhan

doi:10.1109/siu49456.2020.9302494

Cited by 2 publications

(1 citation statement)

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“…Random forest is an ensemble classifier that employs a large number of unpruned decision trees. Each decision tree that composes the forest is created with selected samples from the training dataset with the bootstrapping technique [57]. A randomly selected subset of features is used to separate the data according to the heterogeneity measure.…”

Section: Random Forestmentioning

confidence: 99%

Classification of Imbalanced Offensive Dataset – Sentence Generation for Minority Class with LSTM

Ekinci¹

2022

Sakarya University Journal of Computer and Information Sciences

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The classification of documents is one of the problems studied since ancient times and still continues to be studied. With social media becoming a part of daily life and its misuse, the importance of text classification has started to increase. This paper investigates the effect of data augmentation with sentence generation on classification performance in an imbalanced dataset. We propose an LSTM based sentence generation method, Term Frequency-Inverse Document Frequency (TF-IDF) and Word2vec and apply Logistic Regression (LR), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Extremely Randomized Trees (Extra tree), Random Forest, eXtreme Gradient Boosting (Xgboost), Adaptive Boosting (AdaBoost) and Bagging. Our experiment results on an imbalanced Offensive Language Identification Dataset (OLID) that machine learning with sentence generation significantly outperforms.

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Section: Random Forestmentioning

confidence: 99%