Papers Multi-Label Classification

An Online Universal Classifier for Binary, Multi-class and Multi-label Classification

Classification involves the learning of the mapping function that associates input samples to corresponding target label. There are two major categories of classification problems: Single-label classification and Multi-l…

A Unified View of Multi-Label Performance Measures

Multi-label classification deals with the problem where each instance is associated with multiple class labels. Because evaluation in multi-label classification is more complicated than single-label setting, a number of …

A novel online multi-label classifier for high-speed streaming data applications

In this paper, a high-speed online neural network classifier based on extreme learning machines for multi-label classification is proposed. In multi-label classification, each of the input data sample belongs to one or m…

A Novel Online Real-time Classifier for Multi-label Data Streams

In this paper, a novel extreme learning machine based online multi-label classifier for real-time data streams is proposed. Multi-label classification is one of the actively researched machine learning paradigm that has …

A High Speed Multi-label Classifier based on Extreme Learning Machines

In this paper a high speed neural network classifier based on extreme learning machines for multi-label classification problem is proposed and dis-cussed. Multi-label classification is a superset of traditional binary an…

Multi-Label Classification Method Based on Extreme Learning Machines

In this paper, an Extreme Learning Machine (ELM) based technique for Multi-label classification problems is proposed and discussed. In multi-label classification, each of the input data samples belongs to one or more tha…

Applying Topological Persistence in Convolutional Neural Network for Music Audio Signals

Recent years have witnessed an increased interest in the application of persistent homology, a topological tool for data analysis, to machine learning problems. Persistent homology is known for its ability to numerically…

Densely Connected Convolutional Networks

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In…

Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging

Environmental audio tagging aims to predict only the presence or absence of certain acoustic events in the interested acoustic scene. In this paper we make contributions to audio tagging in two parts, respectively, acous…

Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global Pooling

We trained a deep all-convolutional neural network with masked global pooling to perform single-label classification for acoustic scene classification and multi-label classification for domestic audio tagging in the DCAS…

node2vec: Scalable Feature Learning for Networks

Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progres…

Fully DNN-based Multi-label regression for audio tagging

Acoustic event detection for content analysis in most cases relies on lots of labeled data. However, manually annotating data is a time-consuming task, which thus makes few annotated resources available so far. Unlike au…

An empirical study on large scale text classification with skip-gram embeddings

We investigate the integration of word embeddings as classification features in the setting of large scale text classification. Such representations have been used in a plethora of tasks, however their application in cla…

An Efficient Large-scale Semi-supervised Multi-label Classifier Capable of Handling Missing labels

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,…

Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models

Many complex multi-target prediction problems that concern large target spaces are characterised by a need for efficient prediction strategies that avoid the computation of predictions for all targets explicitly. Example…

Large scale biomedical texts classification: a kNN and an ESA-based approaches

With the large and increasing volume of textual data, automated methods for identifying significant topics to classify textual documents have received a growing interest. While many efforts have been made in this directi…

How is a data-driven approach better than random choice in label space division for multi-label classification?

We propose using five data-driven community detection approaches from social networks to partition the label space for the task of multi-label classification as an alternative to random partitioning into equal subsets as…

Improved Neural Network-based Multi-label Classification with Better Initialization Leveraging Label Co-occurrence

Automatic tagging and retrieval of E-Commerce products based on visual features

bunji at SemEval-2016 Task 5: Neural and Syntactic Models of Entity-Attribute Relationship for Aspect-based Sentiment Analysis