Sentence examples for high classification problem from inspiring English sources

The monkey data were analyzed in a similar way as the human data, except that we collapsed multiple frequency conditions into a binary, low versus high, classification problem (with a chance level of 50%).

We have presented three kinds of genetic fuzzy systems based on Michigan, Pittsburgh and iterative rule learning (IRL) approaches to deal with intrusion detection as a high-dimensional classification problem.

In our study, we apply linear classifiers on this high-dimensional classification problem, and apply the one-versus-the-rest (Bishop et al., 2006) method to convert the multiclass classification problem into a series of binary class problems.

The role of feature selection methods in a high dimensional pattern classification problem is to select the minimum number of features that maximize the recognition accuracy.

The genetic algorithm is employed to find useful fuzzy concepts with high classification performance for classification problems; then, each of classes and patterns can be represented by a fuzzy set of useful fuzzy concepts.

For each noise level, MVPA was carried out within each anatomical ROI using shrinkage discriminant analysis23 as implemented in the R package 'sda.' Shrinkage discriminant analysis is a form of linear discriminant analysis that estimates shrinkage parameters for the variance-covariance matrix of the data, making it suitable for high-dimensional classification problems.

Nevertheless, the solution for such a high-dimensional pattern classification problem is either computationally complex or has an intolerable convergence, by consuming a mass of energy [43].

The MVPA can be modeled as a high-dimensional pattern classification problem to train a classification (or prediction) model based on the fMRI BOLD signals, in which voxels (as features) are identified in response to stimulus or diagnostic conditions (as class labels).

The k-NN algorithm is a nonparametric classification method that can achieve high classification accuracy in problems with non-normal and unknown distributions.

Similarly, [34] concluded that they obtained the highest accuracy for their classification problem using a CFS as compared to other feature selection methods.

It is reasonable that if we want to get big improvement on the micro F1 measure, we must solve the classification problem for high-frequency classes first as an important preliminary step.