2 research outputs found
Towards Scalable and Unified Example-based Explanation and Outlier Detection
When neural networks are employed for high-stakes decision making, it is
desirable for the neural networks to provide explanation for their prediction
in order for us to understand the features that have contributed to the
decision. At the same time, it is important to flag potential outliers for
in-depth verification by domain experts. In this work we propose to unify two
differing aspects of explainability with outlier detection. We argue for a
broader adoption of prototype-based student networks capable of providing an
example-based explanation for its prediction and at the same time identify
regions of similarity between the predicted sample and the examples. The
examples are real prototypical cases sampled from the training set via our
novel iterative prototype replacement algorithm. Furthermore, we propose to use
the prototype similarity scores for identifying outliers. We compare
performances in terms of classification, explanation quality, and outlier
detection of our proposed network with other baselines. We show that our
prototype-based networks beyond similarity kernels deliver meaningful
explanation and promising outlier detection results without compromising
classification accuracy
Similarity Kernels For Nearest Neighbor-Based Outlier Detection
Outlier detection is an important research topic that focuses on detecting abnormal information in data sets and processes. This paper addresses the problem of determining which class of kernels should be used in a geometric framework for nearest neighbor-based outlier detection. It introduces the class of similarity kernels and employs it within that framework. We also propose the use of isotropic stationary kernels for the case of normed input spaces. Two definitions of similarity scores using kernels are given: the k-NN kernel similarity score (kNNSS) and the summation kernel similarity score (SKSS). The paper concludes with preliminary experimental results comparing the performance of kNNSS and SKSS for outlier detection on four data sets. SKSS compared favorably to kNNSS. © 2010 Springer-Verlag Berlin Heidelberg