ACTIVE FEATURE ACQUISITION WITH GENERATIVE SURROGATE MODELS

Abstract

Many real-world situations allow for the acquisition of additional relevant information when making an assessment with limited or uncertain data. However, traditional ML approaches either require all features to be acquired beforehand or regard part of them as missing data that cannot be acquired. In this work, we propose models that perform active feature acquisition (AFA) to improve the prediction assessments at evaluation time. We formulate the AFA problem as a Markov decision process (MDP) and resolve it using reinforcement learning (RL). The AFA problem yields sparse rewards and contains a high-dimensional complicated action space. Thus, we propose learning a generative surrogate model that captures the complicated dependencies among input features to assess potential information gain from acquisitions. We also leverage the generative surrogate model to provide intermediate rewards and auxiliary information to the agent. Furthermore, we extend AFA in a task we coin active instance recognition (AIR) for the unsupervised case where the target variables are the unobserved features themselves and the goal is to collect information for a particular instance in a cost-efficient way. Empirical results demonstrate that our approach achieves considerably better performance than previous state of the art methods on both supervised and unsupervised tasks.

1. INTRODUCTION

A typical machine learning paradigm for discriminative tasks is to learn the distribution of an output, y given a complete set of features, x ∈ R d : p(y | x). Although this paradigm is successful in a multitude of domains, it is incongruous with the expectations of many real-world intelligent systems in two key ways: first, it assumes that a complete set of features has been observed; second, as a consequence, it also assumes that no additional information (features) of an instance may be obtained at evaluation time. These assumptions often do not hold; human agents routinely reason over instances with incomplete data and decide when and what additional information to obtain. For example, consider a doctor diagnosing a patient. The doctor usually has not observed all possible measurements (such as blood samples, x-rays, etc.) for the patient. He/she is not forced to make a diagnosis based on the observed measurements; instead, he/she may dynamically decide to take more measurements to help determine the diagnosis. Of course, the next measurement to make (feature to observe), if any, will depend on the values of the already observed features; thus, the doctor may determine a different set of features to observe from patient to patient (instance to instance) depending on the values of the features that were observed. Hence, not each patient will have the same subset of features selected (as would be the case with typical feature selection). Furthermore, acquiring features typically involves some cost (in time, money and risk), and intelligent systems are expected to automatically balance the cost and the return on improvement of the task performance. In order to more closely match the needs of many real-world applications, we propose an active feature acquisition (AFA) model that not only makes predictions with incomplete/missing features, but also determines what next feature would be the most valuable to obtain for a particular instance. In this work, we formulate the active feature acquisition problem as a Markov decision process (MDP), where the state is the set of currently observed features and the action is the next feature to acquire. We also introduce a special action to indicate whether to stop the acquisition process and make a final prediction. Reinforcement learning is then utilized to optimize the MDP, and the agent learns a policy for selecting which next feature to acquire based on the current state.

