In multi-label learning, each object is represented by a single instance while associated with a set of class labels. Due to the huge (exponential) number of possible label sets for prediction, existing approaches mainly focus on how to exploit label correlations to facilitate the learning process. Nevertheless, an intrinsic characteristic of learning from multi-label data, i.e. the widely-exis...

Dimensionality reduction is an essential step in high-dimensional data analysis. Many dimensionality reduction algorithms have been applied successfully to multi-class and multi-label problems. They are commonly applied as a separate data preprocessing step before classification algorithms. In this paper, we study a joint learning framework in which we perform dimensionality reduction and multi...

Multi-label classification is an appealing and challenging supervised learning problem, where multiple labels, rather than a single label, are associated with an unseen test instance. To remove possible noises in labels and features of high-dimensionality, multi-label dimension reduction has attracted more and more attentions in recent years. The existing methods usually suffer from several pro...

Classifying data hierarchically is an efficient approach to analyze data. Data is usually classified into multiple categories, or annotated with a set of labels. To analyze multi-labeled data, such data must be specified by giving a set of labels as a semantic range. There are some certain purposes to analyze data. This paper shows which multi-labeled data should be the target to be analyzed fo...

Most recent work has been focused on associative classification technique. Most research work of classification has been done on single label data. But it is not appropriate for some real world application like scene classification, bioinformatics, and text categorization. So that here we proposed multi label classification to solve the issues arise in single label classification. That is very ...

We present a novel and scalable label embedding framework for large-scale multi-label learning a.k.a ExMLDS (Extreme Multi-Label Learning using Distributional Semantics). Our approach draws inspiration from ideas rooted in distributional semantics, specifically the Skip Gram Negative Sampling (SGNS) approach, widely used to learn word embeddings for natural language processing tasks. Learning s...

Multi-label classification has received considerable interest in recent years. Multi-label classifiers have to address many problems including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels, as well as exploiting unlabeled data to improve prediction performance. To ...

Semi-supervised multi-label classification has been applied to many real-world applications such as image classification, document classification and so on. In semi-supervised learning, unlabeled samples are added to the training set for enhancing the classification performance, however, noises are introduced simultaneously. In order to reduce this negative effect, the nearest neighbor data edi...

Abstract Radiographs are a versatile diagnostic tool for the detection and assessment of pathologies, treatment planning or navigation localization purposes in clinical interventions. However, their interpretation by radiologists can be tedious error-prone. Thus, wide variety deep learning methods have been proposed to support interpreting radiographs. Mostly, these approaches rely on convoluti...