Journal of Artificial Intelligence Machine Learning and Neural Network, Год журнала: 2024, Номер 46, С. 12 - 26

Опубликована: Окт. 1, 2024

Outlier detection problems have drawn much attention in recent times for their variety of applications. An outlier is a data point that different from the rest and can be detected based on some measure. In years, Artificial Neural Networks (ANN) been used extensively finding outliers more efficiently. This method highly competitive with other methods currently use such as similarity searches, density-based approaches, clustering, distance-based linear methods, etc. this paper, we proposed an extended representation learning neural network. model follows symmetric structure like autoencoder where dimensions are initially increased original then reduced. Root mean square error to compute score. Reconstructed calculated analyzed detect possible outliers. The experimental findings documented by applying it two distinct datasets. performance compared several state-of-art approaches Rand Net, Hawkins, LOF, HiCS, Spectral. Numerical results show outperforms all these terms 5 validation scores, Accuracy (AC), Precision (P), Recall, F1 Score, AUC

Язык: Английский