Hierarchical Multi-label Classification using Fully Associative Ensemble Learning

Zhang, L.; Shah, Sneh; Kakadiaris, Ioannis A.

doi:10.1016/j.patcog.2017.05.007

Cited by 72 publications

(29 citation statements)

References 24 publications

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“…Information from ancestors and closely related siblings in the hierarchy may provide useful information for protein function prediction, including through heterogeneous ensembles. Previous work has utilized this information for advancing individual and ensemble PFP algorithms 37 – 39 , and similar ideas can be used to improve heterogeneous ensembles as well.…”

Section: Discussionmentioning

confidence: 99%

Large-scale protein function prediction using heterogeneous ensembles

et al. 2018

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Heterogeneous ensembles are an effective approach in scenarios where the ideal data type and/or individual predictor are unclear for a given problem. These ensembles have shown promise for protein function prediction (PFP), but their ability to improve PFP at a large scale is unclear. The overall goal of this study is to critically assess this ability of a variety of heterogeneous ensemble methods across a multitude of functional terms, proteins and organisms. Our results show that these methods, especially Stacking using Logistic Regression, indeed produce more accurate predictions for a variety of Gene Ontology terms differing in size and specificity. To enable the application of these methods to other related problems, we have publicly shared the HPC-enabled code underlying this work as LargeGOPred ( https://github.com/GauravPandeyLab/LargeGOPred).

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Section: Discussionmentioning

confidence: 99%

Large-scale protein function prediction using heterogeneous ensembles

et al. 2018

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“…Hierarchical Multi-label Classification (HMC) is also related to the proposed framework. In HMC, each sample has more than one label and all these labels are organized hierarchically in a tree or Direct Acyclic Graph (DAG) [41,42]. Hierarchical information in tree and DAG structures is used to improve classification performance [43,44].…”

Section: Related Workmentioning

confidence: 99%

Patch-based face recognition using a hierarchical multi-label matcher

Zhang¹,

Dou²,

Kakadiaris³

2018

Image and Vision Computing

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This paper proposes a hierarchical multi-label matcher for patch-based face recognition. In signature generation, a face image is iteratively divided into multi-level patches. Two different types of patch divisions and signatures are introduced for 2D facial image and texture-lifted image, respectively. The matcher training consists of three steps. First, local classifiers are built to learn the local matching of each patch. Second, the hierarchical relationships defined between local patches are used to learn the global matching of each patch. Three ways are introduced to learn the global matching: majority voting, 1 -regularized weighting, and decision rule. Last, the global matchings of different levels are combined as the final matching. Experimental results on different face recognition tasks demonstrate the effectiveness of the proposed matcher at the cost of gallery generalization. Compared with the UR2D system, the proposed matcher improves the Rank-1 accuracy significantly by 3% and 0.18% on the UHDB31 dataset and IJB-A dataset, respectively.

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“…During the training phase, the complexity of computing and storing K(z, z) is significant for large size problems. Therefore, a random sample-selection technique introduced in Zhang et al [50] can be applied to reduce the kernel complexity of large-scale datasets. The assumption behind this is to select a small number of samples that could represent the distribution of a large-scale dataset.…”

Section: Fully Associative Learningmentioning

confidence: 99%

Fully Associative Patch-Based 1-to-N Matcher for Face Recognition

Zhang

Kakadiaris

2018

2018 International Conference on Biometrics (ICB)

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This paper focuses on improving face recognition performance by a patch-based 1-to-N signature matcher that learns correlations between different facial patches. A Fully Associative Patch-based Signature Matcher (FAPSM) is proposed so that the local matching identity of each patch contributes to the global matching identities of all the patches. The proposed matcher consists of three steps. First, based on the signature, the local matching identity and the corresponding matching score of each patch are computed. Then, a fully associative weight matrix is learned to obtain the global matching identities and scores of all the patches. At last, the 1 -regularized weighting is applied to combine the global matching identity of each patch and obtain a final matching identity. The proposed matcher has been integrated with the UR2D system for evaluation. The experimental results indicate that the proposed matcher achieves better performance than the current UR2D system. The Rank-1 accuracy is improved significantly by 3% and 0.55% on the UHDB31 dataset and the IJB-A dataset, respectively.

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Hierarchical Multi-label Classification using Fully Associative Ensemble Learning

Cited by 72 publications

References 24 publications

Large-scale protein function prediction using heterogeneous ensembles

Large-scale protein function prediction using heterogeneous ensembles

Patch-based face recognition using a hierarchical multi-label matcher

Fully Associative Patch-Based 1-to-N Matcher for Face Recognition

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