An Outlier Detection Algorithm Based on Local Density Feedback Outlier Factor

Abstract

Neighborhood similarity assumes that adjacent objects should exhibit similar characteristics. To address the issue that previous density-based algorithms neglected neighborhood similarity and global information, resulting in low accuracy for outlier detection, we propose an outlier detection algorithm based on local density feedback outlier factor. Firstly, principal component analysis method is employed combining with natural neighbor search algorithm for density estimation. Subsequently, a density feedback mechanism is introduced for post-processing density values, which defines the density differences within the neighborhood as feedback signals, and update density feedback values for each object based on the principle of neighborhood similarity. Through multiple iterations of feedback, individual objects can aggregate global information. Finally, feedback outlier factor is defined to quantify outlier degrees. Experimental validation demonstrates the effectiveness of the proposed algorithm in enhancing outlier detection performance.