The issue of data imbalance occurs in many real-world applications especially in medical
diagnosis, where normal cases are usually much more than the abnormal cases. To …

In real-world scenarios, different features have different acquisition costs at test time which
necessitates cost-aware methods to optimize the cost and performance tradeoff. This paper …

An essential step in data mining and machine learning is selecting a useful feature subset
from the high-dimensional feature space. Many existing feature selection algorithms only …

In this paper, we propose a sparse representation based Reduced-Reference Image Quality
Assessment (RR-IQA) index for stereoscopic images from the following two perspectives: 1) …

Finding patterns among risk factors and chronic illness can suggest similar causes, provide
guidance to improve healthy lifestyles, and give clues for possible treatments for outliers …

Although the performance of cost-sensitive support vector machine (CS-SVM) has been
demonstrated to approximate to the cost-sensitive Bayes risk, previous CS-SVM methods …

Imbalanced text classification, as practical and essential text classification, is the task to
learn labels or categories for imbalanced text data. Existing imbalanced text classification …

Variable selection plays a dominant role in building forecast models when high-dimensional
data appears. However, how to select important variables from a large number of candidate …

Chronic kidney disease (CKD) is one of the leading causes of death across the globe,
affecting about 10% of the world's adult population. Kidney disease affects the proper …

Nonlinear patterns are challenging to interpret, validate, and are resource-intensive for deep
learning (DL) and machine learning (ML) algorithms to predict chronic illness …