Cost-sensitive Selection of Variables by Ensemble of Model Sequences
Many applications require the collection of data on different variables or measurements over many system performance metrics. We term those broadly as measures or variables. Often data collection along each measure incurs a cost, thus it is desirable to consider the cost of measures in modeling. This is a fairly new class of problems in the area of cost-sensitive learning. A few attempts have been made to incorporate costs in combining and selecting measures. However, existing studies either do not strictly enforce a budget constraint, or are not the most' cost effective. With a focus on classification problem, we propose a computationally efficient approach that could find a near optimal model under a given budget by exploring the most promising' part of the solution space. Instead of outputting a single model, we produce a model schedule -- a list of models, sorted by model costs and expected predictive accuracy. This could be used to choose the model with the best predictive accuracy under a given budget, or to trade off between the budget and the predictive accuracy. Experiments on some benchmark datasets show that our approach compares favorably to competing methods.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Cost-sensitive Hierarchical Clustering for Dynamic Classifier Selection
We consider the dynamic classifier selection (DCS) problem: Given an ensemble of classifiers, we are to choose which classifier to use depending on the particular input vector that we get to classify. The problem is a sp…
ClusteringCost-sensitive Feature Selection for Support Vector Machines
Feature Selection is a crucial procedure in Data Science tasks such as Classification, since it identifies the relevant variables, making thus the classification procedures more interpretable, cheaper in terms of measure…
Classificationfeature selectionFeature Selection Methods for Cost-Constrained Classification in Random Forests
Cost-sensitive feature selection describes a feature selection problem, where features raise individual costs for inclusion in a model. These costs allow to incorporate disfavored aspects of features, e.g. failure rates …
Classificationfeature selectionGeneral ClassificationModel SelectionCost-Sensitive Active Learning for Intracranial Hemorrhage Detection
Deep learning for clinical applications is subject to stringent performance requirements, which raises a need for large labeled datasets. However, the enormous cost of labeling medical data makes this challenging. In thi…
Active LearningComputed Tomography (CT)Dynamic Ensemble Selection Using Fuzzy Hyperboxes
Most dynamic ensemble selection (DES) methods utilize the K-Nearest Neighbors (KNN) algorithm to estimate the competence of classifiers in a small region surrounding the query sample. However, KNN is very sensitive to th…