Systematic construction of anomaly detection benchmarks from real data

Research in anomaly detection suffers from a lack of realis-tic and publicly-available problem sets. This paper discusses what properties such problem sets should possess. It then introduces a methodology for transforming existing classi-fication data sets into ground-truthed benchmark data sets for anomaly detection. The methodology produces data sets that vary along three important dimensions: (a) point diffi-culty, (b) relative frequency of anomalies, and (c) clustered-ness. We apply our generated datasets to benchmark several popular anomaly detection algorithms under a range of dif-ferent conditions. 1

End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression

When intelligent interfaces, such as intelligent desktop assistants, email classifiers, and recommender systems, customize themselves to a particular end user, such customizations can decrease productivity and increase frustration due to inaccurate predictions — especially in early stages when training data is limited. The end user ca improve the learning algorithm by tediously labeling a substantial amount of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose new supervised and semi-supervised learning algorithms based on locally weighted logistic regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. We first evaluate our algorithms against other feature labeling algorithms under idealized conditions using feature labels generated by an oracle. In addition, another of our contributions is an evaluation of feature labeling algorithms under real world conditions using feature labels harvested from actual end users in our user study. Our user study is the first statistical user study for feature labeling involving a large number of end users (43 participants), all of whom have no background in machine learning. Our supervised and semi-supervised algorithms were among the best performers when compared to other feature labeling algorithms in the idealized setting and they are also robust to poor quality feature labels provided by ordinary end users in our study. We also perform an analysis to investigate the relative gains of incorporating the different sources of knowledge available in the labeled training set, the feature labels and the unlabeled data. Together, our results strongly suggest that feature labeling by end users is both viable and effective for allowing end users to improve the learning algorithm behind their customized applications

End-User Feature Labeling via Locally Weighted Logistic Regression

Applications that adapt to a particular end user often make inaccurate predictions during the early stages when training data is limited. Although an end user can improve the learning algorithm by labeling more training data, this process is time consuming and too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose a new learning algorithm based on Locally Weighted Logistic Regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. In our user study, the first allowing ordinary end users to freely choose features to label directly from text documents, our algorithm was more effective than others at leveraging end users’ feature labels to improve the learning algorithm. Our results strongly suggest that allowing users to freely choose features to label is a promising method for allowing end users to improve learning algorithms effectively