Hybrid Modeling to Classify and Detect Outliers on Multilabel Dataset based on Content and Context

Due to the linked various matching categories, news article categorization are a rapidly increasing field of interest in text classification. However, the low-reliability indices and ambiguities related to frequently used province classifiers restrict success in this field. Most of the existing research uses traditional machine learning algorithms. It has weaknesses in training large-scale datasets, and data sparseness often occurs from short texts. Therefore, this study proposed a hybrid model consisting of two models, namely the news article classification and the outlier detection model. The news article classification model used a combination of two deep learning algorithms (Long Short-Term Memory dan Convolutional Neural Network) and outlier classifier model, which was intended to predict the outlier news using a decision tree algorithm. The proposed model's performance was compared against two widely used datasets. The experimental results provide useful insights that open the way for a number of future initiatives.

@article{Efrizoni2022,
  title     = {Hybrid Modeling to Classify and Detect Outliers on Multilabel Dataset based on Content and Context},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {12},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Lusiana Efrizoni and Sarjon Defit and Muhammad Tajuddin},
  doi       = {10.14569/IJACSA.2022.0131267},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131267}
}