A multi-source information fusion model for outlier detection

Abstract

Multi-source information fusion (MSIF) is a useful strategy for combining complimentary data from numerous information sources to produce an overall precise description, which can help with effective decision-making, prediction, and categorization, etc. In order to find the objects that are different from the expected ones after fusion, i.e., anomalies, or outliers, an MSIF model is put forward for outlier detection. This is a two-stage model that includes fusion of multiple information sources and outlier detection of fused data. The first stage uses information sets to construct uncertainty criteria for information source values and combines multiple information sources into a single information source based on the minimum uncertainty strategy. The second stage uses the Gaussian kernel method for possibility modeling based on the fused data to construct knowledge granules. From the perspective of granular computing, outliers in the fused data can be assigned to each knowledge granule. Then, we can find all outliers just by evaluating these knowledge granules. Inspired by this, the fuzzy knowledge measure (FKM) is proposed to evaluate the knowledge granule. Moreover, several metrics are induced on the basis of FKM to describe outliers in knowledge granules and an FKM-based outlier detection algorithm (FKMOD) is designed. Finally, we conduct the experiments on sixteen open access outlier detection datasets. The experimental results show that the proposed FKMOD method has more accurate detection performance than nine classical methods.