Multilabel Classification with R Package mlr

Probst, Philipp; Au, Quay; Casalicchio, Giuseppe; Stachl, Clemens; Bischl, Bernd

doi:10.32614/rj-2017-012

Cited by 22 publications

(35 citation statements)

References 27 publications

(47 reference statements)

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“…We compare the hybrid label-based meta-learning with generalized linear mixed model (HybridLBGLM) approach with the MLKNN [23], binary relevance method (BR) [15], [35], multi-label ferns (Ferns) [36] and randomForestSRC [37]. MLKNN is the arguably the state-of-the-art in instance-based multi-label ranking.…”

Section: A Evaluation Measurementsmentioning

confidence: 99%

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A Multi-Label Classification With Hybrid Label-Based Meta-Learning Method in Internet of Things

2020

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With the widespread adoption of Internet connected devices and the application of Internet of Things (IoT), more and more research efforts focusing on using machine learning techniques in recognizing activities from IoT sensors, especially in solving multi-label classification problems. Without considering the associations among labels, traditional approaches aim to transform the original multi-label classification problem into several single-label classification problems. The loss of information among labels will damage the classification performance. In this paper, we proposed a novel hybrid label-based meta-learning algorithm for multi-label classification based on an ensemble of a cluster algorithm and generalized linear mixed model (GLMM). In this algorithm, the clustering phase is performed to catch the association among labels and to reduce the computational complexity from vast label subsets simultaneously, and the GLMM phase is performed to solve dependence of a subject with multi-labels in training data. The numerical results show that the proposed algorithm outperforms others, especially for cases with relatively large number of labels. INDEX TERMS Clustering, generalized linear mixed model (GLMM), meta-learning, multi-label classification.

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Section: A Evaluation Measurementsmentioning

confidence: 99%

“…Binary relevance method, which is the simplest problem transformation method, learns a binary classifier for each label and then combined all binary classifiers to a multi-label target. We used mlr package [35] to run BR, Ferns and randomForestSRC. Table 3 and 4 summarize the testing results of our experiments.…”

Section: A Evaluation Measurementsmentioning

confidence: 99%

A Multi-Label Classification With Hybrid Label-Based Meta-Learning Method in Internet of Things

2020

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“…Some works use the mlr package, which was not specifically designed for MLC. As a result, it provides only a few multi-label strategies (Probst et al, 2017) and does not support the MLC ARFF format. In fact, it is a general purpose package, with an interface to more than one hundred algorithms that supports several ML tasks (Bischl et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Although it does not contain MLC strategies, it supports the ARFF variation for MLC data, largely used for data mining and machine learning (ML) experiments, and has useful features, such as dataset characterization, MLC evaluation measures, and a rich user interface for the data exploration.Some works use the mlr package, which was not specifically designed for MLC. As a result, it provides only a few multi-label strategies (Probst et al, 2017) and does not support the MLC ARFF format. In fact, it is a general purpose package, with an interface to more than one hundred algorithms that supports several ML tasks (Bischl et al, 2016).…”

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confidence: 99%

The utiml Package: Multi-label Classification in R

Rívolli¹,

Carvalho²

2019

The R Journal

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Learning classification tasks in which each instance is associated with one or more labels are known as multi-label learning. The implementation of multi-label algorithms, performed by different researchers, have several specificities, like input/output format, different internal functions, distinct programming language, to mention just some of them. As a result, current machine learning tools include only a small subset of multi-label decomposition strategies. The utiml package is a framework for the application of classification algorithms to multi-label data. Like the well known MULAN used with Weka, it provides a set of multi-label procedures such as sampling methods, transformation strategies, threshold functions, pre-processing techniques and evaluation metrics. The package was designed to allow users to easily perform complete multi-label classification experiments in the R environment. This paper describes the utiml API and illustrates its use in different multi-label classification scenarios. IntroductionMulti-label classification (MLC) is a classification task where an instance can be simultaneously classified in more than one of the existing classes. Labeled data extracted from several domains, like text, web pages, multimedia (audio, image, videos), and biology are intrinsically multi-labeled. Additionally, the number of application domains with MLC data is growing fast.Many current, real-world data science applications are MLC by nature. They are problems from very diverse domains, like labeling newspaper articles by subject and classification of proteins according to their functions. MLC algorithms have been successfully used in these and other MLC tasks (Diplaris et al., 2005). In a recent application, MLC algorithms were used to recommend food truck cuisines (Rivolli et al., 2017), assuming that a person can have more than one cuisine preference, and with the same level of preference.Despite its growing relevance, there is a lack of comprehensive and easy to use tools for the R environment. A tool frequently used in MLC experiments is MULAN (Tsoumakas et al., 2011), which is a Java library built on top of Weka (Hall et al., 2009) to allow Weka users to deal with MLC data. Its popularity in the research community can be attributed to its ease of use, its large number and variation of its functionalities. The MLC alternative to Python users is the scikit-multilearn (Szymański, 2017), which provides a set of MLC algorithms and an interface for the MULAN library. Although other simpler tools, like MEKA (Read et al., 2016) and general data mining software (Gibaja and Ventura, 2015) include good functionalities to deal with MLC tasks, they address few MLC features and are not available in R.It is important to mention that there are packages that offer some level of support for MLC in R. The most complete is the mldr package, an exploratory tool for the manipulation and analysis of MLC datasets (Charte and Charte, 2015). Although it does not contain MLC strategies, it supports the ARFF variation for MLC ...

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“…We used the random forest machine learning method Qi, 2012) to predict parity reached in dairy cattle at birth and at first calving. The data was analyzed in the statistical program R (Team, 2016), using the packages 'mlr' Probst et al, 2017) to implement 'randomForest' . Random forest was applied by first tuning on the training data, which was the data from 2000 to 2006, and then validating on the 2007 data.…”

Section: Discussionmentioning

confidence: 99%

Predicting survival in dairy cattle using machine learning

Heide¹,

Maria²

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VAN DER HEIDE. (2020). Predicting survival in dairy cattle using machine learning. PhD thesis, Wageningen University, the Netherlands A lthough cows can live to be twenty years old, the average lifespan for a dairy cow is only five to six years. Improving the lifespan of dairy cows would have several benefits such as increasing farm profitability and reducing the environmental impact of milk production. However, the complexity of survival makes it difficult to improve this trait in practice. In this thesis, I proposed using phenotypic prediction of survival to select young cows for the dairy herd, improving survival through increased lifespan of selected cows and better heifer management. The aim of this thesis was to investigate if it was possible to predict survival phenotype accurately enough to be of use in selection. I investigated three different methods to predict survival: multiple logistic regression, random forest and naive Bayes. In chapters two to four of this thesis I predicted the survival trait "survival to second lactation" using all three aforementioned methods. In chapter five, I predicted the survival trait "number of parities reached" using only the random forest method. Random forest and naive Bayes proved the best methods for predicting survival to second lactation, although predictive performance overall was low. The correlations between predictions for individual cows were much lower than expected, which indicated that the models predicted individual cows differently. Therefore, in chapter four I investigated if combining the results into an ensemble could improve predictive performance. An ensemble using multiple logistic regression resulted in the largest increase in performance, although none of the explored ensemble methods improved performance consistently across datasets. I further investigated if there was a benefit in including genomic information or a farm-specific effect. In chapter two, I investigated the benefit of combining genomic and phenotypic information. Genomic breeding values especially improved the prediction of survival early in life, with breeding values for fertility and longevity remained informative even after first calving. In chapter five I tested several different methods to include a farm effect and described the advantages and disadvantages of the various approaches. The results of this thesis provide valuable insights in the challenges of predicting survival traits and the suitability of various (machine learning) methods for the prediction of survival in dairy cattle.

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Multilabel Classification with R Package mlr

Cited by 22 publications

References 27 publications

A Multi-Label Classification With Hybrid Label-Based Meta-Learning Method in Internet of Things

A Multi-Label Classification With Hybrid Label-Based Meta-Learning Method in Internet of Things

The utiml Package: Multi-label Classification in R

Predicting survival in dairy cattle using machine learning

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