Papers Classification with Costly Features
“Classification with Costly Features” 태그가 달린 논문 4편 · 필터 해제
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms
The fseval Python package allows benchmarking Feature Selection and Feature Ranking algorithms on a large scale, and facilitates the comparison of multiple algorithms in a systematic way. In particular, fseval enables us…
Automated Feature EngineeringBenchmarkingClassification with Costly FeaturesDistributed Computing+3Classification with Costly Features in Hierarchical Deep Sets
Classification with Costly Features (CwCF) is a classification problem that includes the cost of features in the optimization criteria. Individually for each sample, its features are sequentially acquired to maximize acc…
ClassificationClassification with Costly FeaturesDeep Reinforcement LearningGeneral Classification+4Classification with Costly Features as a Sequential Decision-Making Problem
This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample budget. Inspired by real-world use-cases…
ClassificationClassification with Costly FeaturesDecision MakingDeep Reinforcement Learning+5Classification with Costly Features using Deep Reinforcement Learning
We study a classification problem where each feature can be acquired for a cost and the goal is to optimize a trade-off between the expected classification error and the feature cost. We revisit a former approach that ha…
ClassificationClassification with Costly FeaturesDecision MakingDeep Reinforcement Learning+6