Sentence examples for cost sensitive learning from inspiring English sources

Minimal cost decision tree construction plays a crucial role in cost sensitive learning.

In cost sensitive learning, misclassification of the marginal class is assigned a high cost which the algorithm then attempts to lessen.

The imbalanced UCI Machine Learning Repository datasets are used in many standard cost sensitive learning papers in literature [2, 3, 10].

After weighting scheme is applied, WELM classifier can push the separating boundary from the minority class towards the majority class [28] As a result, the WELM classifier can be generalized to cost sensitive learning by assigning different weight [28].

Cost sensitive learning can be implemented using an inherently cost-sensitive classification approach, or using a "wrapper" which converts an otherwise cost-agnostic classifier into a cost sensitive one.

This makes it behave very different from the conventional static picture of cost sensitive learning — e.g. an error in the immediate time frame is now costlier than that same error occurring at a future time.

As an example of the sensitivity that can be achieved with the sampling methods and cost-sensitive learning while maintaining a reasonable specificity, Table  9 shows the performance of the classifiers with the highest sensitivity and a specificity of at least 0.5.

A survey of cost-sensitive learning techniques over the years is covered in [1].

These techniques have been traditionally proposed under three different perspectives: data treatment, adaptation of algorithms, and cost-sensitive learning.

We use two approaches to deal with the imbalance problem: sampling methods and cost-sensitive learning.

While it is a challenge to construct many suitable transformation functions for the costs with diverse units, this paper designs a heterogeneous-cost sensitive learning (HCSL) algorithm to make split attribute selection more effective.