Large Number Of Labels Research Articles

MLHN: A Hypernetwork Model for Multi-Label Classification

Multi-label classification has attracted significant attentions in machine learning. In multi-label classification, exploiting correlations among labels is an essential but nontrivial task. First, labels may be correlated in various degrees. Second, the scalability may suffer from the large number of labels, because the number of combinations among labels grows exponentially as the number of labels increases. In this paper, a multi-label hypernetwork (MLHN) is proposed to deal with these problems. By extending the traditional hypernetwork model, MLHN can represent arbitrary order correlations among labels. The classification model of MLHN is simple and the computational complexity of MLHN is linear with respect to the number of labels, which contribute to the good scalability of MLHN. We perform experiments on a variety of datasets. The results illustrate that the proposed MLHN achieves competitive performances against state-of-the-art multi-label classification algorithms in terms of both effectiveness and scalability with respect to the number of labels.

LI-MLC: a label inference methodology for addressing high dimensionality in the label space for multilabel classification.

Multilabel classification (MLC) has generated considerable research interest in recent years, as a technique that can be applied to many real-world scenarios. To process them with binary or multiclass classifiers, methods for transforming multilabel data sets (MLDs) have been proposed, as well as adapted algorithms able to work with this type of data sets. However, until now, few studies have addressed the problem of how to deal with MLDs having a large number of labels. This characteristic can be defined as high dimensionality in the label space (output attributes), in contrast to the traditional high dimensionality problem, which is usually focused on the feature space (by means of feature selection) or sample space (by means of instance selection). The purpose of this paper is to analyze dimensionality in the label space in MLDs, and to present a transformation methodology based on the use of association rules to discover label dependencies. These dependencies are used to reduce the label space, to ease the work of any MLC algorithm, and to infer the deleted labels in a final postprocessing stage. The proposed process is validated in an extensive experimentation with several MLDs and classification algorithms, resulting in a statistically significant improvement of performance in some cases, as will be shown.

Labeling Complicated Objects: Multi-View Multi-Instance Multi-Label Learning

Multi-Instance Multi-Label (MIML) is a learning framework where an example is associated with multiple labels and represented by a set of feature vectors (multiple instances). In the formalization of MIML learning, instances come from a single source (single view). To leverage multiple information sources (multi-view), we develop a multi-view MIML framework based on hierarchical Bayesian Network, and derive an effective learning algorithm based on variational inference. The model can naturally deal with examples in which some views could be absent (partial examples). On multi-view datasets, it is shown that our method is better than other multi-view and single-view approaches particularly in the presence of partial examples. On single-view benchmarks, extensive evaluation shows that our method is highly competitive or better than other MIML approaches on labeling examples and instances. Moreover, our method can effectively handle datasets with a large number of labels.

Two stage architecture for multi-label learning

A common approach to solving multi-label learning problems is to use problem transformation methods and dichotomizing classifiers as in the pair-wise decomposition strategy. One of the problems with this strategy is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming, especially in learning problems with a large number of labels. To tackle this problem, we propose a Two Stage Architecture (TSA) for efficient multi-label learning. We analyze three implementations of this architecture the Two Stage Voting Method (TSVM), the Two Stage Classifier Chain Method (TSCCM) and the Two Stage Pruned Classifier Chain Method (TSPCCM). Eight different real-world datasets are used to evaluate the performance of the proposed methods. The performance of our approaches is compared with the performance of two algorithm adaptation methods (Multi-Label k-NN and Multi-Label C4.5) and five problem transformation methods (Binary Relevance, Classifier Chain, Calibrated Label Ranking with majority voting, the Quick Weighted method for pair-wise multi-label learning and the Label Powerset method). The results suggest that TSCCM and TSPCCM outperform the competing algorithms in terms of predictive accuracy, while TSVM has comparable predictive performance. In terms of testing speed, all three methods show better performance as compared to the pair-wise methods for multi-label learning.

Random k-Labelsets for Multilabel Classification

A simple yet effective multilabel learning method, called label powerset (LP), considers each distinct combination of labels that exist in the training set as a different class value of a single-label classification task. The computational efficiency and predictive performance of LP is challenged by application domains with large number of labels and training examples. In these cases, the number of classes may become very large and at the same time many classes are associated with very few training examples. To deal with these problems, this paper proposes breaking the initial set of labels into a number of small random subsets, called labelsets and employing LP to train a corresponding classifier. The labelsets can be either disjoint or overlapping depending on which of two strategies is used to construct them. The proposed method is called RAkEL (RAndom k labELsets), where k is a parameter that specifies the size of the subsets. Empirical evidence indicates that RAkEL manages to improve substantially over LP, especially in domains with large number of labels and exhibits competitive performance against other high-performing multilabel learning methods.

Recognition of spectral amplitude codes by frequency swept coherent detection for flexible optical label switching

We propose a new method of recognizing spectral amplitude codes by using optical coherent detection with a frequency swept local light source oscillator. Our proposed method offer a substantial simplification in terms of required components to built optical label processing units with enhanced flexibility to accommodate for wavelength tuneability and a large number of labels. We present a performance analysis, comparison with conventional spectral codes recognition methods, based on computer simulation results. We consider a payload bit rate of 40 Gb/s.

ECO-LABELLING IN THE EU: A COMPARATIVE STUDY OF THE PULP AND PAPER INDUSTRY IN THE UK AND SWEDEN

A feature of the EC's Fifth Environmental Action Programme is the move away from the use of legislative measures towards the adoption of voluntary and economic market-based instruments to control the environmental impact of industrial activities. Eco-labelling has been adopted by the EC to promote products with reduced environmental impacts throughout their life cycle and to provide consumers with reliable information about the impact of products on the environment. Products that are deemed to be more environmentally friendly than their competitors are identified by a single, distinctive eco-label. The use of one main eco-label across the EU should reduce the confusion that has been created by the use of a large number of labels and environmental claims by producers and retailers. This ESRC-funded research aims to assess whether an eco-award system is an effective means of improving environmental performance, with specific reference to the pulp and paper industry. Comparisons of attitudes to the eco-labelling scheme are being made in France, Germany, Sweden and the UK. Interviews are being conducted with industry, national governments, the EC, competent bodies, consumer organizations and environmental pressure groups. In this way, it will be possible to assess whether an EU-wide scheme is either practical or desirable in one economic sector. Research to date indicates that although the concepts underlying eco-labelling are sound, the existing scheme has several serious weaknesses. If these are not addressed, the credibility of the scheme will be reduced and the real commitment of the EC to environmental policy questioned.

Product label inspection using transputers

A product label inspection system implemented on a transputer network is described. Two data routing algorithms are considered, one for a general M × N array and one for a customized setup of transputers. Data transfer results for various image sizes are provided. Reliable and fast label inspection algorithms are described for rectangular, acute-angled and oval-shaped product labels, and their implementation on both the general and the customized networks are discussed. Simulated test results are provided to illustrate the present capability of the system for inspecting large numbers of labels per second.

NAD + labeled immunoassays

Among the large number of labels which have been used in immunochemistry, NAD +, the coenzyme of many dehydrogenases, has been the label of choice. It allows the design of homogeneous immunoassays for haptens and proteins and it is detected by chemical amplification using enzymatic cycling procedures.

A Tropical Book Worm in Florida: Neogastrallus librinocens Fisher, in Florida (Anobiidae, Col.)

In Sanford, his work had to do largely with the ecology of the celery leaf-tier, and he demonstrated that during most years the pest is held down by parasites and other factors, controlled largely by the weather during the winters. He left Florida in 1928, for the University of Arizona, where he became Dean of the College of Agriculture and Director of the Agricultural Experiment Station. Dr. Ball was a tireless worker. He once called to the attention of the writer, the large number of labels on his leaf-hoppers which bore a date of a vacation day. His extensive collection of leaf-hoppers and related insects is now in the National Museum. -Ed.