Outlier Detection using Artificial Rabbit Optimizer with Hopfield Neural Network
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Abstract
Nowadays, anomaly detection becomes difficult in real-time computer networks as there is a continuous increase of high-volume, high-speed data streams and high dimensional, where ground truth data is unavailable. Outlier detection can be referred to as detecting data points in the dataset that differ from other data. This is significant in many domains, including statistics, data mining, and machine learning, as outliers can have a major effect on data analysis outcomes. Several OD solutions are presented that could compute anomaly scores while managing the data stream. Therefore, this study shows an artificial rabbit optimizer with Hopfield neural network-based outlier detection with data classification (AROHNN-ODDC) technique. The presented AROHNN-ODDC technique focuses on removing outliers and classifies high-dimensional data. In the given AROHNN-ODDC technique, the initial stage of data pre-processing is performed. Next, the OD process is performed by the Local Outlier Factor (LOF) model. The ARO approach is followed to select a subset of features effectively. Finally, the HNN classifier is used for data classification purposes. A wide range of simulations was carried out against benchmark datasets to assess the enhanced data classification results of the proposed method. The experimental outcomes stated the promising performance of the AROHNN-ODDC algorithm over other existing techniques.
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