Efficient Online Decision Tree Learning with Active Feature Acquisition
Constructing decision trees online is a classical machine learning problem. Existing works often assume that features are readily available for each incoming data point. However, in many real world applications, both feature values and the labels are unknown a priori and can only be obtained at a cost. For example, in medical diagnosis, doctors have to choose which tests to perform (i.e., making costly feature queries) on a patient in order to make a diagnosis decision (i.e., predicting labels). We provide a fresh perspective to tackle this practical challenge. Our framework consists of an active planning oracle embedded in an online learning scheme for which we investigate several information acquisition functions. Specifically, we employ a surrogate information acquisition function based on adaptive submodularity to actively query feature values with a minimal cost, while using a posterior sampling scheme to maintain a low regret for online prediction. We demonstrate the efficiency and effectiveness of our framework via extensive experiments on various real-world datasets. Our framework also naturally adapts to the challenging setting of online learning with concept drift and is shown to be competitive with baseline models while being more flexible.
Code (0)
등록된 구현이 없습니다.
Tasks
Medical DiagnosisSimilar Papers 제목 키워드 기반
A Reinforcement Learning Approach to Online Learning of Decision Trees
Online decision tree learning algorithms typically examine all features of a new data point to update model parameters. We propose a novel alternative, Reinforcement Learning- based Decision Trees (RLDT), that uses Reinf…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Feature Acquisition using Monte Carlo Tree Search
Feature acquisition algorithms address the problem of acquiring informative features while balancing the costs of acquisition to improve the learning performances of ML models. Previous approaches have focused on calcula…
Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task
We introduce a challenging decision-making task that we call active acquisition for multimodal temporal data (A2MT). In many real-world scenarios, input features are not readily available at test time and must instead be…
Decision MakingInformativenessA Survey on Active Feature Acquisition Strategies
Active feature acquisition studies the challenge of making accurate predictions while limiting the cost of collecting complete data. By selectively acquiring only the most informative features for each instance, these st…
Decision MakingSurveyTowards Cost Sensitive Decision Making
Many real-world situations allow for the acquisition of additional relevant information when making decisions with limited or uncertain data. However, traditional RL approaches either require all features to be acquired …
Decision Making