multi label classification


Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to.

A k-nearest neighbor based algorithm for multi – label classification .
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In multi – label learning, each instance in the training set is associated with a set of labels, and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, a multi – label lazy learning approach named ML-kNN is presented, which is

Matrix completion for multi – label image classification
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Recently, image categorization has been an active research topic due to the urgent need to retrieve and browse digital images via semantic keywords. This paper formulates image categorization as a multi – label classification problem using recent advances in matrix

Multi – label classification on tree-and dag-structured hierarchies
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Many real-world applications involve multilabel classification , in which the labels are organized in the form of a tree or directed acyclic graph (DAG). However, current research efforts typically ignore the label dependencies or can only exploit the dependencies in tree

Efficient multi – label classification with many labels
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In multi – label classification , each sample can be associated with a set of class labels. When the number of labels grows to the hundreds or even thousands, existing multi – label classification methods often become computationally inefficient. In recent years, a number of

Multi – label classification via feature-aware implicit label space encoding
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To tackle a multi – label classification problem with many classes, recently label space dimension reduction (LSDR) is proposed. It encodes the original label space to a low- dimensional latent space and uses a decoding process for recovery. In this paper, we

From softmax to sparsemax: A sparse model of attention and multi – label classification
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We propose sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities. After deriving its properties, we show how its Jacobian can be efficiently computed, enabling its use in a network trained with backpropagation. Then, we

Optimizing the F-measure in multi – label classification : Plug-in rule approach versus structured loss minimization
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We compare the plug-in rule approach for optimizing the Fβ-measure in multi – label classification with an approach based on structured loss minimization, such as the structured support vector machine (SSVM). Whereas the former derives an optimal prediction from a

Multi – label text classification with a mixture model trained by EM
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In many important document classification tasks, documents each be associated with multiple class labels. This paper describes a Bayesian classification approach in which the multiple classes that comprise a document are represented by a mixture model. While the

Multi -instance multi – label learning with application to scene classification
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In this paper, we formalize multi -instance multi – label learning, where each training example is associated with not only multiple instances but also multiple class labels. Such a problem can occur in many real-world tasks, eg an image usually contains multiple patches each of

Multi – label classification methods for multi -target regression
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Real world prediction problems often involve the simultaneous prediction of multiple target variables using the same set of predictive variables. When the target variables are binary, the prediction task is called multi – label classification while when the target variables are

A ranking-based KNN approach for multi – label classification
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Multi – label classification has attracted a great deal of attention in recent years. This paper presents an interesting finding, namely, being able to identify neighbors with trustable labels can significantly improve the classification accuracy. Based on this finding, we propose a k

Reader Perspective Emotion Analysis in Text through Ensemble based Multi – Label Classification Framework.
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Multiple emotions are often triggered in readers in response to text stimuli like news article. In this paper, we present a novel method for classifying news sentences into multiple emotion categories using an ensemble based multi – label classification technique called

Multi – label classification with label constraints
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We extend the multi – label classification setting with constraints on labels. This leads to two new machine learning tasks: First, the label constraints must be properly integrated into the classification process to improve its performance and second, we can try to automatically

Identification of label dependencies for multi – label classification
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The main feature distinguishing multi – label classification from a regular classification task is that a number of labels have to be predicted simultaneously. Thus, it is obviously important to exploit potential dependencies between labels. However, surprisingly only a few of the

Multi – label Image Classification with A Probabilistic Label Enhancement Model.
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In this paper, we present a novel probabilistic label enhancement model to tackle multi – label image classification problem. Recognizing multiple objects in images is a challenging problem due to label sparsity, appearance variations of the objects and occlusions. We

Multi – label classification with error-correcting codes
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We formulate a framework for applying error-correcting codes (ECC) on multi – label classification problems. The framework treats some base learners as noisy channels and uses ECC to correct the prediction errors made by the learners. An immediate use of the

A comparative analysis of classification methods to multi – label tasks in different application domains
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In traditional classification problems (single- label ), patterns are usually associated with a single label from a set of two or more classes. When an example can simultaneously belong to more than one class ( label ), this classification problem is known as multi – label

An improved multi – label classification method based on SVM with delicate decision boundary
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Multi – label classification problem is an extension of traditional multi -class classification problem in which the classes are not mutually exclusive and each sample belong to several classes simultaneously. Such problems occur in many important applications. Some

Obtaining bipartitions from score vectors for multi – label classification
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However, some of the algorithms that learn from multi – label data, can only output a score for each label , so they cannot be readily used in applications that require bipartitions. In addition, several of the recent state-of-the-art multi – label classification algorithms, actually


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