关键词: 多示例学习, 权重机制, 特征, 惩罚策略

Abstract: In the multi-instance learning framework, the training data set consists of several packages. The package contains multiple examples represented by attribute-value pairs. The system learns multiple examples in the package. The traditional local outlier detection algorithm based on multi-instance learning applies the multi-instance learning framework to the data set, transforming the multi-example problem into a single example problem. However, in the conversion process of the example package, the ratio of the internal feature lengths is used as the weighting mechanism, examples of significant impact on the results do not be inspected, or the reasons be analyzed or their weights be adjusted dynamically, affecting the outlier detection effect. For this problem, in order to fully adapt to the internal distribution characteristics of data, a local outlier improvement algorithm FWMIL-LOF based on multi-instance learning is proposed. The algorithm adopts MIL（Multi-Instance Learning） framework, which introduces a weight function that describes the importance of data in the conversion process of the example package, and adjusts the weight function by defining a penalty strategy. Thus, the weight of examples with different feature attributes is determined in the belonging package. In the actual enterprise’s real-time acquisition and monitoring system, through simulation analysis, and compared with other classical local outlier detection algorithms, the improvement of the outlier detection effect of the improved algorithm is verified.

Key words: Multi-Instance Learning（MIL）, weight mechanism, feature, penalty strategy