How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification?
AbstractWe 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 (
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Szymański, P.; Kajdanowicz, T.; Kersting, K. How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification? Entropy 2016, 18, 282.
Szymański P, Kajdanowicz T, Kersting K. How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification? Entropy. 2016; 18(8):282.Chicago/Turabian Style
Szymański, Piotr; Kajdanowicz, Tomasz; Kersting, Kristian. 2016. "How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification?" Entropy 18, no. 8: 282.
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