Five-Region Rough Isolation Forest with Multi-Strategy Feature Optimization for High-Dimensional Anomaly Detection

Abstract

: Anomaly detection is used to identify data points deviating from normal patterns and plays a significant role in fields such as fault diagnosis, biomedicine, and cybersecurity. However, anomaly detection tasks in practical applications often involve high-dimensional data which presents challenges such as severe feature redundancy, hidden anomaly patterns, and difficulty in capturing uncertainty. These issues make it difficult to effectively identify anomalies, thereby limiting the model’s discriminative power and stability. To address these challenges, we propose a high-dimensional anomaly detection model, MSFO-EIF, integrating multi-strategy feature optimization with rough set modeling. First, in the feature space optimization stage, we incorporate label information and employ a Maximum Relevance Minimum Redundancy (mRMR) pre-screening and an uncertainty clustering mechanism to filter and structurally organize the original features, thereby reducing redundancy and retaining key discriminative information. Second, in the feature selection phase, we construct a multi-strategy co-evolution mechanism to optimize feature subsets within a label-guided search space, thereby mitigating the risk of local optima. Finally, in the anomaly detection stage, multi-region partitioning and rough set concepts are introduced to characterize the sample distribution structure at a fine granularity, thereby enhancing the ability to identify boundary samples and weak anomalies. Experimental results show that the proposed model outperforms other anomaly detection models, including KNN, LOF, IBBA-EIF, and RRSM, on multiple high-dimensional datasets. Specifically, MSFO-EIF achieves the improvements of 1.68%, 1.33%, 1.69%, 2.67% and 1.87% in accuracy, precision, F1-score, AUC and AUC-PR, respectively, highlighting its superior detection performance and robustness.