Partial label learning (PLL) aims to learn from inexact data annotations where each training example is associated with a coarse candidate set. Due its practicability, many PLL algorithms have been proposed in recent literature. Most prior works attempt identify the ground-truth labels sets and classifier trained afterward by fitting features of examples their exact labels. From different persp...

Partial multi-label learning (PML), which tackles the problem of prediction models from instances with overcomplete noisy annotations, has recently started gaining attention research community. In this paper, we propose a novel adversarial model, PML-GAN, under generalized encoder-decoder framework for partial learning. The PML-GAN model uses disambiguation network to identify irrelevant labels...

Retailers’ brands maker with private label have significantly boosted market share in recent years. Creating new brands for goods or services provide differentiation with similar distributors. The main aim of this paper is to test which component can be more effective in consumers’ purchase intention based on using private label for goods’ image. This research data was collected by prior st...

background: quality assurance in the hematology laboratory is a must to ensure laboratory users of reliable test results with high degree of precision and accuracy. even after so many advances in hematology laboratory practice, pre-analytical errors remain a challenge for practicing pathologists. this study was undertaken with an objective to evaluate the types and frequency of preanalytical er...

When correlating the samples with the corresponding class labels, canonical correlation analysis (CCA) can be used for supervised feature extraction and subsequent classification. Intuitively, different encoding modes for class label can result in different classification performances. However, actually, when the samples in each class share a common class label as in usual cases, a unified form...

Multi-label learning deals with data associated with multiple labels simultaneously. Previous work on multi-label learning assumes that for each instance, the “full” label set associated with each training instance is given by users. In many applications, however, to get the full label set for each instance is difficult and only a “partial” set of labels is available. In such cases, the appeara...

Many machine learning systems rely on data collected in the wild from untrusted sources, exposing the learning algorithms to data poisoning. Attackers can inject malicious data in the training dataset to subvert the learning process, compromising the performance of the algorithm producing errors in a targeted or an indiscriminate way. Label flipping attacks are a special case of data poisoning,...

The aim of this paper is to elaborate on the important issue of label dependence in multi-label classification (MLC). Looking at the problem from a statistical perspective, we claim that two different types of label dependence should be distinguished, namely conditional and unconditional. We formally explain the differences and connections between both types of dependence and illustrate them by...