Abstract
Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common drawbacks: (1) the user-specific parameter for the number of clusters may incur the effective problem; (2) SVM may bring a high computational cost when utilized as the classifier builder. In this paper, we propose an algorithm, namely multi-instance multi-label (MIML)-extreme learning machine (ELM), to address the problems. To our best knowledge, we are the first to utilize ELM in the MIML problem and to conduct the comparison of ELM and SVM on MIML. Extensive experiments have been conducted on real datasets and synthetic datasets. The results show that MIMLELM tends to achieve better generalization performance at a higher learning speed.
Highlights
When utilizing machine learning to solve practical problems, we often consider an object as a feature vector
multi-instance multi-label (MIML) is a framework for learning with complicated objects and has been proven to be effective in many applications
The existing two-phase MIML approaches may suffer from the effectiveness problem arising from the user-specific cluster number and the efficiency problem arising from the high computational cost
Summary
When utilizing machine learning to solve practical problems, we often consider an object as a feature vector. Taking a protein as an object, a domain as an instance and each biological function as a label, the protein function prediction problem exactly matches the MIML learning task. In this context, multi-instance multi-label learning was proposed [1]. More difficult than two other multi-learning frameworks, MIML studies the ambiguity in terms of both the input space (i.e., instance space) and the output space (i.e., label space), while. Vector Machine (SVM) as the classifiers builder This two-phase framework has been successfully applied to many real-world applications and has been shown to be effective [5].
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