Credit risk evaluation is a difficult task to predict default probabilities and deduce risk
classification, and many classification methods and techniques have already been applied …

This paper introduces a novel model for spectral clustering to solve the problem of poor
connectivity among points within the same cluster as this can negatively impact the …

When dealing with complex and redundant data classification problems, many classifiers
cannot provide high predictive accuracy and interpretability. We also find that the least …

For classification problems, training accurate and sparse models in limited time has been a
longstanding challenge. When a large number of irrelevant and redundant features are …

Since many classifier methods cannot identify and remove redundant observations and
unrelated attributes from data, they usually give more inconsistent classification between …

For supervised learning problems, a unified kernel sparse representation framework is
proposed. It is applicable to almost all supervised learners in order to look for kernel …

Training accurate and explainable models in limited time has been a longstanding
challenge for classification problems. One of the most popular techniques to cope with this …

For many classification methods, different features in these classification models are usually
organized into a flat and equal-length vector while semantically restrictive relations or …