Sentence examples for neighbour classification from inspiring English sources

Prototype selection problem consists of reducing the size of databases by removing samples that are considered noisy or not influential on nearest neighbour classification tasks.

The traditional VAA method of voting recommendation is referred as 'Party Coding', while the KNN refers to k-nearest neighbour classification [44, 46].

Nearest neighbour classification is known to be problematic in high-dimensional data [2]; therefore, following the recommendations of Aggarwal et al. [2], the (L_{0.1}) distance metric is chosen.

Sreluge employs a neighbour classification system and a time series forecasting technique to isolate polluters and a combinatorial technique to decode data packets in the presence of polluters before the isolation is complete.

Finally, we can see in Table 7 that the k-nearest neighbour classification (KNN) has better Recall, F-measure and MAP scores than the HMM classifier in the Greek dataset.

Further data mining of the transcriptome analysis included K-nearest neighbour classification.

On the other hand, the mathematical bases of nearest neighbour classifications are quite different from discriminant analysis.

These predictors utilize machine learning methods such as support vector machine and k-Nearest Neighbours classification.

K-nearest neighbours classification with leave-one-out cross-validation was chosen in a first approach.

The proposed evaluator is based on a wrapper approach that utilises two performance measures (MCC and AUC) in conjunction with the k-Nearest Neighbours classification model (kNN).

The advantages and disadvantages of clustering based, nearest neighbour based, classification based and spectral anomaly detection techniques are discussed in [35].