Sentence examples for label ranking from inspiring English sources

calibrated label ranking.

We compare RPC to existing label ranking methods, which are based on scoring individual labels instead of comparing pairs of labels.

This paper addresses the issue by developing a novel learning-to-rank model with a limited number of training data, which combines the strength of cost-sensitive label ranking methods with the power of low-rank matrix recovery theories.

This work focuses on a particular learning scenario called label ranking, where the problem is to learn a mapping from instances to rankings over a finite number of labels.

Characteristic learning tasks include label ranking (see example below), instance ranking, and object ranking.

Moreover, discretization is also expected to improve the predictive accuracy for classification [4] and Label Ranking [5].

It is calling for a consumer label, ranked A-G, that shows the levels of air pollution from all manufacturers' cars.

The metaheuristics are coordinated to progress through five distinct steps that constitute hCSO: (1) initialization; (2) exchange; (3) modification; (4) replacement; (5) metaheuristic labelling, ranking and carry forward.

We stop labeling, ranking, and rating children, teachers, and schools.

To evaluate the forecast results of ML-kNN comparing with traditional kNN, the following criteria [ 19] are used: Average_Precision evaluates the average fraction of syndrome labels ranked above a particular label which actually is in the label set as follows: The performance is perfect when it is 1; the bigger the value of average precision, the better the performance of classifiers.

5) Average precision: defined as: a v e r a g e p r e c i s i o n f = 1 p ∑ i = 1 p 1 Y i ∑ y ∈ Y i L i r a n k f x i, y, L i = y ′ | r a n k f x i, y ′ ≤ r a n k f x i, y, y ′ ∈ Y i, which is used to evaluate the average fraction of labels ranked above a particular label y∈ Y which actually are in Y.