Journal
ENTROPY
Volume 18, Issue 8, Pages -Publisher
MDPI
DOI: 10.3390/e18080282
Keywords
label space clustering; label co-occurrence; label grouping; multi-label classification; clustering; machine learning; random k-label sets; ensemble classification
Categories
Funding
- European Union within the European Social Fund
- European Commission under the 7th Framework Programme, Coordination and Support Action [316097]
- European Union's Horizon research and innovation program under the Marie Sklodowska-Curie Grant [691152]
- National Science Centre [DEC-2013/09/B/ST6/02317]
- Faculty of Computer Science and Management, Wroclaw University of Science and Technology
- DFG Collaborative Research Center Project A6 Resource Efficient Analysis of Graphs [SFB 876]
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We propose using five data-driven community detection approaches from social networks to partition the label space in the task of multi-label classification as an alternative to random partitioning into equal subsets as performed by RAkELd. We evaluate modularity-maximizing using fast greedy and leading eigenvector approximations, infomap, walktrap and label propagation algorithms. For this purpose, we propose to construct a label co-occurrence graph (both weighted and unweighted versions) based on training data and perform community detection to partition the label set. Then, each partition constitutes a label space for separate multi-label classification sub-problems. As a result, we obtain an ensemble of multi-label classifiers that jointly covers the whole label space. Based on the binary relevance and label powerset classification methods, we compare community detection methods to label space divisions against random baselines on 12 benchmark datasets over five evaluation measures. We discover that data-driven approaches are more efficient and more likely to outperform RAkELd than binary relevance or label powerset is, in every evaluated measure. For all measures, apart from Hamming loss, data-driven approaches are significantly better than RAkELd (alpha = 0.05), and at least one data-driven approach is more likely to outperform RAkELd than a priori methods in the case of RAkELd's best performance. This is the largest RAkELd evaluation published to date with 250 samplings per value for 10 values of RAkELd parameter k on 12 datasets published to date.
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