BatchRank: A Novel Batch Mode Active Learning Framework for Hierarchical Classification

Chakraborty, S and Balasubramanian, Vineeth N and Adepu, R S and Panchanathan, S and Ye, J (2015) BatchRank: A Novel Batch Mode Active Learning Framework for Hierarchical Classification. In: 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

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Active learning algorithms automatically identify the salient and exemplar instances from large amounts of unlabeled data and thus reduce human annotation effort in inducing a classification model. More recently, Batch Mode Active Learning (BMAL) techniques have been proposed, where a batch of data samples is selected simultaneously from an un- labeled set. Most active learning algorithms assume a at label space, that is, they consider the class labels to be in- dependent. However, in many applications, the set of class labels are organized in a hierarchical tree structure, with the leaf nodes as outputs and the internal nodes as clusters of outputs at multiple levels of granularity. In this paper, we propose a novel BMAL algorithm (BatchRank) for hi- erarchical classification. The sample selection is posed as an NP-hard integer quadratic programming problem and a convex relaxation (based on linear programming) is derived, whose solution is further improved by an iterative truncated power method. Finally, a deterministic bound is established on the quality of the solution. Our empirical results on sev- eral challenging, real-world datasets from multiple domains, corroborate the potential of the proposed framework for real- world hierarchical classification applications.

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IITH Creators:
IITH CreatorsORCiD
Balasubramanian, Vineeth NUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Active Learning, Hierarchical Classification, Optimization
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 13 Aug 2015 10:05
Last Modified: 25 Apr 2018 05:42
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