Abstract In some applications, acquiring covariates comes at a cost which is not negligible. For example in the medical domain, order to classify whether patient has diabetes or not, measuring glucose tolerance can be expensive. Assuming that of each covariate, and misclassification specified by user, our goal minimize (expected) total classification, i.e. plus acquired covariates. We formalize...

Fine-grained categorization is an essential field in classification, a subfield of object recognition that aims to differentiate subordinate classes. image classification concentrates on distinguishing between similar, hard-to-differentiate types or species, for example, flowers, birds, specific animals such as dogs cats, and identifying airplane makes models. An important step towards fine-gra...

Land cover maps are of vital importance to various fields such as land use policy development, ecosystem services, urban planning and agriculture monitoring, which mainly generated from remote sensing image classification techniques. Traditional usually needs tremendous computational resources, often becomes a huge burden the community. Undoubtedly cloud computing is one best choices for classi...

There are many sensing challenges for which one must balance the effectiveness of a given measurement with the associated sensing cost. For example, when performing a diagnosis a doctor must balance the cost and benefit of a given test (measurement), and the decision to stop sensing (stop performing tests) must account for the risk to the patient and doctor (malpractice) for a given diagnosis b...

We design an active learning algorithm for cost-sensitive multiclass classification: problems where different errors have different costs. Our algorithm, COAL, makes predictions by regressing to each label’s cost and predicting the smallest. On a new example, it uses a set of regressors that perform well on past data to estimate possible costs for each label. It queries only the labels that cou...

As a recommended practice of AACE International, the Cost Estimate Classification System provides guidelines for applying the general principles of estimate classification to asset cost estimates. Asset cost estimates typically involve estimates for capital investment, and exclude operating and life-cycle evaluations. The Cost Estimate Classification System maps the phases and stages of asset c...