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

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 can 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.