3 accepted papers
We propose a method that sequentially acquires features and selects a classifier for label assignment in data instances of related variables. The objective is to accurately infer the values of such variables (labels), ensuring at the same time that the expected total feature acquisition cost is mini…
We present a sequential acquisition of features and experts framework for datum–wise classification. The goal is to accurately assign labels for each instance, minimizing the acquisition cost of features and experts. An expert uses domain knowledge to make decisions. Starting from a prior belief, fe…
We introduce a supervised machine learning framework for sequential datum–wise joint feature selection and classification. Our proposed approach sequentially acquires features one at a time during testing until it decides that acquiring more features will not improve label assignment. At that point,…