Multi-label learning

In this paper we focus on the hotel sectors and help them process these huge chunks of data in the form of customer reviews and help them derive useful information. The data pre-processing involves the scrapping of reviews from different sites and storing them and also check the correctness of the regular expression of the reviews. Our modelling employed includes three machine learning algorithms namely Naive Bayes, Support vector machine (svm) and Logistic regression. These three models improve the accuracy of the model as well as its robustness. The main idea of using these models are that the reviews are labelled so that the hotel management need not waste loads of time reading all the reviews. Instead the important reviews can be arranged based on their polarity and the important topic discussed in the review can be highlighted. So that it is easy for the management to analyse both the positive as well as the negative reviews. Sentiment polarity is incorporated to arrange the reviews based on the sentiment the review establishes. This paper helps the world to properly analyse the feedbacks and the reviews given by the customers.

http://www.researchmoz.us/global-label-market-2014-2018-report.html

Global Label market to grow at a CAGR of 4.13 percent over the period 2014-2018. One of the key factors contributing to this market growth is the increasing demand for packaged food and beverages. The Global Label market has also been witnessing the increasing demand from emerging countries. However, the highly fragmented nature of the market could pose a challenge to the growth of this market. Global Label Market 2014-2018, has been prepared based on an in-depth market analysis with inputs from industry experts. The report covers the APAC region, the Americas, Europe, and the ROW; it also covers the Global Label market landscape and its growth prospects in the coming years. The report also includes a discussion of the key vendors operating in this market.

Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.

Multi-label learning has received significant attention in the research community over the past few years: this has resulted in the development of a variety of multi-label learning methods. In this paper, we present an extensive experimental comparison of 12 multi-label learning methods using 16 evaluation measures over 11 benchmark datasets. We selected the competing methods based on their previous usage by the community, the representation of different groups of methods and the variety of basic underlying machine learning methods. Similarly, we selected the evaluation measures to be able to assess the behavior of the methods from a variety of view-points. In order to make conclusions independent from the application domain, we use 11 datasets from different domains. Furthermore, we compare the methods by their efficiency in terms of time needed to learn a classifier and time needed to produce a prediction for an unseen example. We analyze the results from the experiments using Friedman and Nemenyi tests for assessing the statistical significance of differences in performance. The results of the analysis show that for multi-label classification the best performing methods overall are random forests of predictive clustering trees (RF-PCT) and hierarchy of multi-label classifiers (HOMER), followed by binary relevance (BR) and classifier chains (CC). Furthermore, RF-PCT exhibited the best performance according to all measures for multi-label ranking. The recommendation from this study is that when new methods for multi-label learning are proposed, they should be compared to RF-PCT and HOMER using multiple evaluation measures.

Abstract. Automated annotation of scientific publications in real-world digital libraries requires dealing with challenges such as large number of concepts and training examples, multi-label training examples and hierarchical structure of concepts. BioASQ is a European project that contributes a large-scale biomedical publications corpus for working on these challenges. This paper documents the participation of our team to the large-scale biomedical semantic indexing task of BioASQ.

A common approach for solving multi-label classification problems using problem-transformation methods and dichotomizing classifiers is the pair-wise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming especially in classification problems with large number of labels. To tackle this problem we propose a two stage voting architecture (TSVA) for efficient pair-wise multiclass voting to the multi-label setting, which is closely related to the calibrated label ranking method. Four different real-world datasets (enron, yeast, scene and emotions) were used to evaluate the performance of the TSVA. The performance of this architecture was compared with the calibrated label ranking method with majority voting strategy and the quick weighted voting algorithm (QWeighted) for pair-wise multi-label classification. The results from the experiments suggest that the TSVA significantly outperforms the concurrent algorithms in term of testing speed while keeping comparable or offering better prediction performance.

Multi-label classification has rapidly attracted interest in the machine learning literature, and there are now a large number and considerable variety of methods for this type of learning. We present Meka: an open-source Java framework based on the well-known Weka library. Meka provides interfaces to facilitate practical application, and a wealth of multi-label classifiers, evaluation metrics, and tools for multi-label experiments and development. It supports multi-label and multi-target data, including in incremental and semi-supervised contexts.

Identification of electrical appliance usage(s) from the meter panel power reading has become an area of study in its own right. Many approaches over the years have used signal processing approaches at a high sampling rate (1 second typically) to evaluate the appliance load signature and subsequently used pattern recognition techniques for identification from (a) previously trained classifier(s). The proposed approach tries to identify the usage of high power consuming appliance(s) by using the aggregate power consumption at 10 minutes interval from the meter panel. The novelty of the approach lies in using a time series windowing approach which gives addition information about an aggregate power state. The usage of hour of the day as input to the systems also takes into account the temporal behavior of residential users. The usage of Multi-label classification approach for identification is also new for this domain. The model is tested over the IRISE data set and the results are encouraging. Due to its low sampling rate with time stamped aggregate power at 10 minutes scale as the only input from the user, the proposed approach is both practical and affordable.

In this paper we focus on the hotel sectors and help them process these huge chunks of data in the form of customer reviews and help them derive useful information. The data pre-processing involves the scrapping of reviews from different sites and storing them and also check the correctness of the regular expression of the reviews. Our modelling employed includes three machine learning algorithms namely Naive Bayes, Support vector machine (svm) and Logistic regression. These three models improve the accuracy of the model as well as its robustness. The main idea of using these models are that the reviews are labelled so that the hotel management need not waste loads of time reading all the reviews. Instead the important reviews can be arranged based on their polarity and the important topic discussed in the review can be highlighted. So that it is easy for the management to analyse both the positive as well as the negative reviews. Sentiment polarity is incorporated to arrange the reviews based on the sentiment the review establishes. This paper helps the world to properly analyse the feedbacks and the reviews given by the customers.

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 be-longs to one or more than one of the target labels. The traditional binary and multi-class classification where each sample belongs to only one target class forms the subset of multi-label classification. Multi-label classification problems are far more complex than binary and multi-class classification problems, as both the number of target labels and each of the target labels corresponding to each of the input samples are to be identified. The proposed work exploits the high-speed nature of the extreme learning machines to achieve real-time multi-label classification of streaming data. A new threshold-based online sequential learning algorithm is proposed for high speed and streaming data classification of multi-label problems. The proposed method is experimented with six different datasets from different application domains such as multimedia, text, and biology. The hamming loss, accuracy, training time and testing time of the proposed technique is compared with nine different state-of-the-art methods. Experimental studies shows that the proposed technique outperforms the existing multi-label classifiers in terms of performance and speed.

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.

Multi-label classification on data sets with large number of labels is a practically viable and intractable problem. This paper presents an optimization method for the multi-label classification process for data with a high number of labels. The newly proposed method starts with label grouping using community detection methods on interconnectedness graph of labels based on support sizes for every pair of labels. The grouping process is based on modularity-oriented community detection methods. Next the data instances are classified separately for each label community and the resulting labellings are merged afterwards. Both theoretical analysis and experimental results are provided. Experimental results comparing common classification methods to proposed Modularity-based Label Grouping (MLG) with embedded Binary Relevance, executed on on differentiated data sets show a performance increase by 27-41% compared to standard binary relevance, by 72-81% compared to RAkel and by several dozens compared to ECOC-BR-BCH with none or negligible difference in classification quality.

To investigate how young children learn categorical semantic relations between words, 4- to 7-year-olds were taught four labels for novel categories in an “alien” microworld. After two play sessions, where each label was given, with defining information, at least 20 times, comprehension and production were tested. Results of two experiments show that 6-7-year- olds learned more words and correct semantic relations than 4-5-year-olds. The exclusion relation between contrasting category labels was easy to learn, and some findings suggested that hierarchical words are more easily learned than overlapping ones. Both studies showed no advantage to explicitly telling children semantic relations between words (e.g., “All fegs are wuddles.”). The results qualify a common assumption that preschool children have precocious abilities to infer word meaning; such an ability does not seem to extend to semantic relations between words.

In this paper we focus on the hotel sectors and help them process these huge chunks of data in the form of customer reviews and help them derive useful information. The data pre-processing involves the scrapping of reviews from different sites and storing them and also check the correctness of the regular expression of the reviews. Our modelling employed includes three machine learning algorithms namely Naive Bayes, Support vector machine (svm) and Logistic regression. These three models improve the accuracy of the model as well as its robustness. The main idea of using these models are that the reviews are labelled so that the hotel management need not waste loads of time reading all the reviews. Instead the important reviews can be arranged based on their polarity and the important topic discussed in the review can be highlighted. So that it is easy for the management to analyse both the positive as well as the negative reviews. Sentiment polarity is incorporated to arrange the re...

In this paper we focus on the hotel sectors and help them process these huge chunks of data in the form of customer reviews and help them derive useful information. The data pre-processing involves the scrapping of reviews from different sites and storing them and also check the correctness of the regular expression of the reviews. Our modelling employed includes three machine learning algorithms namely Naive Bayes, Support vector machine (svm) and Logistic regression. These three models improve the accuracy of the model as well as its robustness. The main idea of using these models are that the reviews are labelled so that the hotel management need not waste loads of time reading all the reviews. Instead the important reviews can be arranged based on their polarity and the important topic discussed in the review can be highlighted. So that it is easy for the management to analyse both the positive as well as the negative reviews. Sentiment polarity is incorporated to arrange the reviews based on the sentiment the review establishes. This paper helps the world to properly analyse the feedbacks and the reviews given by the customers.

Multi-label learning has received significant attention in the research community over the past few years: this has resulted in the development of a variety of multi-label learning methods. In this paper, we present an extensive experimental comparison of 12 multi-label learning methods using 16 evaluation measures over 11 benchmark datasets. We selected the competing methods based on their previous usage by the community, the representation of different groups of methods and the variety of basic underlying machine learning methods. Similarly, we selected the evaluation measures to be able to assess the behavior of the methods from a variety of view-points. In order to make conclusions independent from the application domain, we use 11 datasets from different domains. Furthermore, we compare the methods by their efficiency in terms of time needed to learn a classifier and time needed to produce a prediction for an unseen example. We analyze the results from the experiments using Friedman and Nemenyi tests for assessing the statistical significance of differences in performance. The results of the analysis show that for multi-label classification the best performing methods overall are random forests of predictive clustering trees (RF-PCT) and hierarchy of multi-label classifiers (HOMER), followed by binary relevance (BR) and classifier chains (CC). Furthermore, RF-PCT exhibited the best performance according to all measures for multi-label ranking. The recommendation from this study is that when new methods for multi-label learning are proposed, they should be compared to RF-PCT and HOMER using multiple evaluation measures.

A controlled environment based on known properties of the dataset used by a learning algorithm is useful to empirically evaluate machine learning algorithms. Synthetic (artificial) datasets are used for this purpose. Although there are publicly available frameworks to generate synthetic single-label datasets, this is not the case for multi-label datasets, in which each instance is associated with a set of labels usually correlated. This work presents Mldatagen, a multi-label dataset generator framework we have implemented, which is publicly available to the community. Currently, two strategies have been implemented in Mldatagen: hypersphere and hypercube. For each label in the multi-label dataset, these strategies randomly generate a geometric shape (hypersphere or hypercube), which is populated with points (instances) randomly generated. Afterwards, each instance is labeled according to the shapes it belongs to, which defines its multi-label. Experiments with a multi-label classification algorithm in six synthetic datasets illustrate the use of Mldatagen.

Interactive classification aims at introducing user preferences in the learning process to produce individualized outcomes more adapted to each user's behaviour than the fully automatic approaches. The current interactive classification systems generally adopt a singlelabel classification paradigm that constrains items to span one label at a time and consequently limit the user's expressiveness while he/she interacts with data that are inherently multi-label. Moreover, the experimental evaluations are mainly subjective and closely depend on the targeted use cases and the interface characteristics. This paper presents the first extensive study of the impact of the interactivity constraints on the performances of a large set of twelve well-established multi-label learning methods. We restrict ourselves to the evaluation of the classifier predictive and time-computation performances while the number of training examples regularly increases and we focus on the beginning of the classification task where few examples are available. The classifier performances are evaluated with an experimental protocol independent of any implementation environment on a set of twelve multi-label benchmarks of various sizes from different domains. Our comparison shows that four classifiers can be distinguished for the prediction quality: RF-PCT (Random Forest of Predictive Clustering Trees, Kocev (2012)), EBR (Ensemble of Binary Relevance, (Read et al., 2011)), CLR (Calibrated Label Ranking, Fürnkranz et al. (2008)) and MLkNN (Multi-label kNN, Zhang and Zhou (2007)) with an advantage for the first two ensemble 1 classifiers. Moreover, only RF-PCT competes with the fastest classifiers and is therefore considered as the most promising classifier for an interactive multi-label learning system.

Multilabel classification is a relatively recent subfield of machine learning. Unlike to the classical approach, where instances are labeled with only one category, in multilabel classification, an arbitrary number of categories is chosen to label an instance. Due to the problem complexity (the solution is one among an exponential number of alternatives), a very common solution (the binary method) is frequently used, learning a binary classifier for every category, and combining them all afterwards. The assumption taken in this solution is not realistic, and in this work we give examples where the decisions for all the labels are not taken independently, and thus, a supervised approach should learn those existing relationships among categories to make a better classification. Therefore, we show here a generic methodology that can improve the results obtained by a set of independent probabilistic binary classifiers, by using a combination procedure with a classifier trained on the co-occurrences of the labels. We show an exhaustive experimentation in three different standard corpora of labeled documents (Reuters-21578, Ohsumed-23 and RCV1), which present noticeable improvements in all of them, when using our methodology, in three probabilistic base classifiers.

Stratified sampling is a sampling method that takes into account the existence of disjoint groups within a population and produces samples where the proportion of these groups is maintained. In single-label classification tasks, groups are differentiated based on the value of the target variable. In multi-label learning tasks, however, where there are multiple target variables, it is not clear how stratified sampling could/should be performed. This paper investigates stratification in the multi-label data context. It considers two stratification methods for multi-label data and empirically compares them along with random sampling on a number of datasets and based on a number of evaluation criteria. The results reveal some interesting conclusions with respect to the utility of each method for particular types of multi-label datasets.

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.

Stratified sampling is a sampling method that takes into account the existence of disjoint groups within a population and produces samples where the proportion of these groups is maintained. In single-label classification tasks, groups are differentiated based on the value of the target variable. In multi-label learning tasks, however, where there are multiple target variables, it is not clear how stratified sampling could/should be performed. This paper investigates stratification in the multi-label data context. It considers two stratification methods for multi-label data and empirically compares them along with random sampling on a number of datasets and based on a number of evaluation criteria. The results reveal some interesting conclusions with respect to the utility of each method for particular types of multi-label datasets.