The current evaluation protocol of long-tailed visual recognition trains the classification
model on the long-tailed source label distribution and evaluates its performance on the …

Class imbalance is one of the challenging problems for machine learning algorithms. When
learning from highly imbalanced data, most classifiers are overwhelmed by the majority …

In real-world applications the number of examples in one class may overwhelm the other
class, but the primary interest is usually on the minor class. Cost-sensitive learning has been …

Deep neural networks perform well in artificially-balanced datasets, but real-world data often
has a long-tailed distribution. Recent studies have focused on developing unbiased …

The recognition of microRNA (miRNA)-binding residues in proteins is helpful to understand
how miRNAs silence their target genes. It is difficult to use existing computational method to …

Background: Recent studies have revealed lifestyle behavioral risk factors that can be
modified to reduce the risk of dementia. As modification of lifestyle takes time, early …

Predicting student performance (PSP) is an important task in Student Modeling where we
would like to know whether the students solve the given problems (tasks) correctly, so that …

Learning with noisy labels aims to ensure model generalization given a label-corrupted
training set. The sample selection strategy achieves promising performance by selecting a …

ABSTRACT Automated Essay Scoring (AES) research efforts primarily focus on feature
engineering and the building of machine learning models to attain higher consensus with …

Learning-based solutions for long-tailed recognition face difficulties in generalizing on
balanced test datasets. Due to imbalanced data prior, the learned\textit {a posteriori} …