Multi-label classification using a cascade of stacked autoencoder and extreme learning machines
•Three phase cascade of neural networks for multi-label classification.•Network model includes stacked autoencoders and extreme learning machines.•Stacked autoencoder reduces complex input features to appropriate representation.•Multi-label extreme learning machine used for soft classification.•Soft...
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| Vydané v: | Neurocomputing (Amsterdam) Ročník 358; s. 222 - 234 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
17.09.2019
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| Predmet: | |
| ISSN: | 0925-2312, 1872-8286 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | •Three phase cascade of neural networks for multi-label classification.•Network model includes stacked autoencoders and extreme learning machines.•Stacked autoencoder reduces complex input features to appropriate representation.•Multi-label extreme learning machine used for soft classification.•Soft classification scores mapped to hard labels with an extreme learning machine.
This article introduces a cascade of neural networks for classification of multi-label data. Two types of networks, namely, stacked autoencoder (SAE) and extreme learning machine (ELM) have been incorporated in the proposed system. ELM is a compact and efficient single-label classifier which seems to lose its efficiency while dealing with multi-label data. This happens due to the complex nature of the multi-label data, which makes it difficult for the smaller networks to interpret it accurately. In our proposed work, we attempt to deal with few of the bottlenecks faced while handling multi-label data. Thus, we aim to enhance the performance of a stand-alone multi-label extreme learning machine (MLELM) by collaborating it with other networks. There are three basic phases in the proposed method: feature encoding, soft classification and class score approximation. In the first step, an SAE network is employed to generate a discriminating and reduced input representation of the multi-label data. This makes the data compact and more manageable for the successive stages. This data in turn is used by an MLELM in the next phase for the prediction of soft labels. In the final step, to improve the prediction capability of the previous network, a novel approach of approximating the class score is proposed using an additional MLELM. Comprehensive experimental evaluation of the proposed approach has been performed on seven datasets against eleven relevant algorithms, and overall it displays a promising performance. |
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| ISSN: | 0925-2312 1872-8286 |
| DOI: | 10.1016/j.neucom.2019.05.051 |