Multilabel Classification

Herrera, Francisco; Charte, Francisco; Rivera, Antonio J.; Jesús, María José del

doi:10.1007/978-3-319-41111-8

Cited by 94 publications

(78 citation statements)

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“…According to (1), the number of output nodes of the proposed DNN architecture for the publicly available UJIIndoorLoc dataset at the University of California, Irvine (UCI), Machine Learning Repository 4 is given by 118 (i.e., the sum of the number of buildings (3), the maximum of the numbers of floors of the buildings (5), and the maximum of the numbers of locations 5 on the floors (110)), which is smaller than the number of output nodes of the DNN architecture based on multi-class classification (i.e., 905 6 ). Note that the difference could be much larger if the UJIIndoorLoc dataset covers all the buildings on the Jaume I University (UJI) campus where the data were collected.…”

Section: Wi-fi Fingerprintingmentioning

confidence: 99%

“…For convenience, we combine the SpaceID and the RELATIVEPOSITION into one and mention it as location throughout the paper so that the three identifiers for building, floor, and location uniquely determine the position of a location. 6 There are slight differences between the statistics of the UJIIndoorLoc dataset described in [7] and those of the publicly available dataset at the UCI Machine Learning Repository. 7 When σ = 0, there is no filtering (i.e., including all κ reference points); when σ = 1, only the reference point with the largest value is considered during the location coordinates estimation.…”

Section: Endnotesmentioning

confidence: 99%

“…We propose a new DNN architecture consisting of a stacked autoencoder (SAE) [4] for the reduction of feature space dimension and a feed-forward multi-label classifier [5,6] for the scalable building/floor classification and floor-level location estimation and evaluate its performance using the UJIIndoorLoc dataset [7].…”

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confidence: 99%

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A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting

2018

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Background:One of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings is a scalable indoor localization technique. In this paper, we report the current status of our investigation on the use of deep neural networks (DNNs) for the scalable building/floor classification and floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the hierarchical nature of the building/floor estimation and floor-level coordinates estimation of a location, we propose a new DNN architecture consisting of a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification of building/floor/location, on which the multi-building and multi-floor indoor localization system based on Wi-Fi fingerprinting is built. Results: We evaluate the performance of building/floor estimation and floor-level coordinates estimation of a given location using the UJIIndoorLoc dataset covering three buildings with four or five floors in the Jaume I University (UJI) campus, Spain. Experimental results demonstrate the feasibility of the proposed DNN-based indoor localization system, which can provide near state-of-the-art performance using a single DNN. Conclusions:The proposed scalable DNN architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting can achieve near state-of-the-art performance with just a single DNN and enables the implementation with lower complexity and energy consumption at mobile devices.

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Section: Wi-fi Fingerprintingmentioning

confidence: 99%

Section: Endnotesmentioning

confidence: 99%

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A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting

2018

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“…For a given label, a high SCUMBLE score represents a large difference between the frequencies of all the other co-occurring labels. In general, datasets with high scores are problematic for classification tasks [18]. However, for datasets characterized by low SCUMBLE score, resampling can reduce unbalancedness [18].…”

Section: Research Datamentioning

confidence: 99%

“…In general, datasets with high scores are problematic for classification tasks [18]. However, for datasets characterized by low SCUMBLE score, resampling can reduce unbalancedness [18]. CADO mean SCUMBLE score is 0.11.…”

Section: Research Datamentioning

confidence: 99%

On using machine learning to identify knowledge in API reference documentation

Fucci

Mollaalizadehbahnemiri

Maalej

2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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Using API reference documentation like JavaDoc is an integral part of software development. Previous research introduced a grounded taxonomy that organizes API documentation knowledge in 12 types, including knowledge about the Functionality, Structure, and Quality of an API. We study how well modern text classification approaches can automatically identify documentation containing specific knowledge types. We compared conventional machine learning (k-NN and SVM) and deep learning approaches trained on manually-annotated Java and .NET API documentation (n = 5,574). When classifying the knowledge types individually (i.e., multiple binary classifiers) the best AUPRC was up to 87%. The deep learning and SVM classifiers seem complementary. For four knowledge types (Concept, Control, Pattern, and Non-Information), SVM clearly outperforms deep learning which, on the other hand, is more accurate for identifying the remaining types. When considering multiple knowledge types at once (i.e., multi-label classification) deep learning outperforms naïve baselines and traditional machine learning achieving a MacroAUC up to 79%. We also compared classifiers using embeddings pre-trained on generic text corpora and StackOverflow but did not observe significant improvements. Finally, to assess the generalizability of the classifiers, we re-tested them on a different, unseen Python documentation dataset. Classifiers for Functionality, Concept, Purpose, Pattern, and Directive seem to generalize from Java and .NET to Python documentation. The accuracy related to the remaining types seems API-specific. We discuss our results and how they inform the development of tools for supporting developers sharing and accessing API knowledge. Published article: https://doi.org/10.1145/3338906.3338943Software developers reuse software libraries and frameworks through Application Programming Interfaces (APIs). They often rely on reference documentation to identify which API elements are relevant for the task at hand, how the API can be instantiated, configured, and combined [1]. Compared to other knowledge sources, such as tutorials and Q&A portals, reference documentation like JavaDoc and PyDoc are considered the official API technical documentation. They provide detailed and fundamental information about API elements, components, operations, and structures [2, 3]. Figure 1: A reference documentation page in the JDK API annotated with the knowledge types it contains. DirectiveAs API documentation can be thousands of pages long [4,5], accessing relevant knowledge can be tedious and timeconsuming [1]. Moreover, the information necessary to accomplish a task can be scattered across the documentation pages of multiple elements, such as classes, methods, and properties. Thus, developers try to use other sources to fulfill their information needs. For example, although the Java Development Kit (JDK) API documentation contains more than 7,000 pages, as of early 2019, there are more than 3 million StackOverflow posts tagged as java.Over the last decade, sof...

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Explaining the performance of multilabel classification methods with data set properties

Bogatinovski

Todorovski

Džeroski

et al. 2022

Int J of Intelligent Sys

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Meta learning generalizes the empirical experience with different learning tasks and holds promise for providing important empirical insight into the behavior of machine learning algorithms. In this paper, we present a comprehensive meta‐learning study of data sets and methods for multilabel classification (MLC). MLC is a practically relevant machine learning task where each example is labeled with multiple labels simultaneously. Here, we analyze 40 MLC data sets by using 50 meta features describing different properties of the data. The main findings of this study are as follows. First, the most prominent meta features that describe the space of MLC data sets are the ones assessing different aspects of the label space. Second, the meta models show that the most important meta features describe the label space, and, the meta features describing the relationships among the labels tend to occur a bit more often than the meta features describing the distributions between and within the individual labels. Third, the optimization of the hyperparameters can improve the predictive performance, however, quite often the extent of the improvements does not always justify the resource utilization.

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Multilabel Classification

Cited by 94 publications

References 0 publications

A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting

A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting

On using machine learning to identify knowledge in API reference documentation

Explaining the performance of multilabel classification methods with data set properties

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