Neural Network For Multi-label Classification Research Articles

SSLpheno: a self-supervised learning approach for gene-phenotype association prediction using protein-protein interactions and gene ontology data.

Medical genomics faces significant challenges in interpreting disease phenotype and genetic heterogeneity. Despite the establishment of standardized disease phenotype databases, computational methods for predicting gene-phenotype associations still suffer from imbalanced category distribution and a lack of labeled data in small categories. To address the problem of labeled-data scarcity, we propose a self-supervised learning strategy for gene-phenotype association prediction, called SSLpheno. Our approach utilizes an attributed network that integrates protein-protein interactions and gene ontology data. We apply a Laplacian-based filter to ensure feature smoothness and use self-supervised training to optimize node feature representation. Specifically, we calculate the cosine similarity of feature vectors and select positive and negative sample nodes for reconstruction training labels. We employ a deep neural network for multi-label classification of phenotypes in the downstream task. Our experimental results demonstrate that SSLpheno outperforms state-of-the-art methods, especially in categories with fewer annotations. Moreover, our case studies illustrate the potential of SSLpheno as an effective prescreening tool for gene-phenotype association identification. https://github.com/bixuehua/SSLpheno.

Robust fused hypergraph neural networks for multi-label classification

Deep neural networks have been adopted in multi-label classification for their excellent performance, however, existing methods fail to comprehensively utilize the high-order correlations between instances and the high-order correlations between labels, and these methods are difficult to deal with label noise effectively. We propose a novel end-to-end deep framework named Robust Fused Hypergraph Neural Networks for Multi-Label Classification (RFHNN), which can effectively utilize the two kinds of high-order correlations and adopt them to mitigate the impact of label noise. In RFHNN, Hypergraph Neural Networks (HNNs) are adopted to mine and utilize the high-order correlations of the instances in the feature space and the label space respectively. The high-order correlations of the instances can not only improve the accuracy of the classification and the discrimination of the proposed model, but also lay the foundation for the subsequent noise correction module. Meanwhile, a hypergraph construction method based on the Apriori algorithm is proposed to realize Hypergraph Neural Networks (HNNs), which can mine robust second-order and high-order label correlations effectively. Effective classifiers are learned based on the correlations between the labels, which will not only improve the accuracy of the model, but can also enhance the subsequent noise correction module. In addition, we have designed a noise correction module in the networks. With the help of the high-order correlations among the instances and the effective classifier, the framework can effectively correct the noise and improve the robustness of the model. Extensive experimental results on datasets demonstrate that our proposed approach is better than the state-of-the-art multi-label classification algorithms. When dealing with the multi-label training datasets with noise in the label space, our proposed method also has great performance.

Learning label-specific features via neural network for multi-label classification

Learning label-specific features via neural network for multi-label classification

ENSOCOM: Ensemble of Multi-Output Neural Network’s Components for Multi-Label Classification

Multitasking and multioutput neural networks models jointly learn related classification tasks from a shared structure. Hard parameters sharing is a multitasking approach that shares hidden layers between multiple task-specific outputs. The output layers’ weights are essential in transforming aggregated neurons outputs into tasks labels. This paper redirects the multioutput network research to prove that the ensemble of output layers prediction can improve network performance in classifying multi-label classification tasks. The network’s output layers initialized with different weights simulate multiple semi-independent classifiers that can make non-identical label sets predictions for the same instance. The ensemble of a multi-output neural network that learns to classify the same multi-label classification task per output layer can outperform an individual output layer neural network. We propose an ensemble strategy of output layers components in the multi-output neural network for multi-label classification (ENSOCOM). The baseline and proposed models are selected based on the size of the hidden layer and the number of output layers to evaluate the proposed method comprehensively. The ENSOCOM method improved the performance of the neural networks on five different multi-label datasets based on several evaluation metrics. The methods presented in this work can substitute the standard labels representation and predictions generation of any neural network.

Randomized neural networks for multilabel classification

Multilabel classification is a supervised learning problem in which input instances belong to multiple output labels. In this paper, we propose noniterative randomization-based neural networks for multilabel classification. These multilabel neural networks are named as Multilabel Random Vector Functional Link Network (ML-RVFL), Multilabel Kernelized Random Vector Functional Link Network (ML-KRVFL), Multilabel Broad Learning System (ML-BLS), and Multilabel Fuzzy Broad Learning System (ML-FBLS). The output weights of these neural networks are computed using pseudoinverse. At the output layer, multilabel classification is performed by using an adaptive threshold function. The computation of output weights using pseudoinverse retains the faster computation power of these algorithms compared to iterative learning algorithms. The adaptive threshold function used in the proposed approach can consider the correlation among the output labels and the whole dataset for threshold computation. Five multilabel evaluation metrics evaluate the proposed multilabel neural networks on 12 benchmark datasets of various domains such as text, image, and genomics. The ML-KRVFL provides the overall best Friedman rankings on five evaluation metrics followed by ML-RVFL, ML-FBLS, and ML-BLS, respectively. Based on the experimentation results, the proposed ML-KRVFL, ML-RVFL, ML-FBLS, and ML-BLS perform better than other relevant multilabel approaches in the mentioned order.The proposed approaches are faster than other state-of-the-art iterative approaches and noniterative approaches in terms of running time.

Deep Attention Neural Network for Multi-Label Classification in Unmanned Aerial Vehicle Imagery

The multi-label classification problem in Unmanned Aerial Vehicle (UAV) images is particularly challenging compared to single-label classification due to its combinatorial nature. To tackle this issue, we propose in this paper a deep learning approach based on encoder-decoder neural network architecture with channel and spatial attention mechanisms. Specifically, the encoder module which is based on a pre-trained convolutional neural network (CNN) has the task to transform the input image to a set of feature maps using an opportune feature combination. To improve the feature representation further, this module incorporates a squeeze excitation (SE) layer for modelling the interdependencies between the channels of the feature maps. The decoder module which is based on a long short terms memory (LSTM) network has the task of generating, in a sequential way, the classes present in the image. At each time step, it predicts the next class-label by aligning its hidden state to the corresponding region in the image by means of an adaptive spatial attention mechanism. The experiments carried out on two UAV datasets with a spatial resolution of 2-cm show that our method is promising in predicting the labels present in the image while attending the relevant objects in the image. Additionally, it is able to provide better classification results compared to state-of-the-art methods.

A comparative study of fuzzy PSO and fuzzy SVD-based RBF neural network for multi-label classification

In multi-label classification problems, every instance is associated with multiple labels at the same time. Binary classification, multi-class classification and ordinal regression problems can be seen as unique cases of multi-label classification where each instance is assigned only one label. Text classification is the main application area of multi-label classification techniques. However, relevant works are found in areas like bioinformatics, medical diagnosis, scene classification and music categorization. There are two approaches to do multi-label classification: The first is an algorithm-independent approach or problem transformation in which multi-label problem is dealt by transforming the original problem into a set of single-label problems, and the second approach is algorithm adaptation, where specific algorithms have been proposed to solve multi-label classification problem. Through our work, we not only investigate various research works that have been conducted under algorithm adaptation for multi-label classification but also perform comparative study of two proposed algorithms. The first proposed algorithm is named as fuzzy PSO-based ML-RBF, which is the hybridization of fuzzy PSO and ML-RBF. The second proposed algorithm is named as FSVD-MLRBF that hybridizes fuzzy c-means clustering along with singular value decomposition. Both the proposed algorithms are applied to real-world datasets, i.e., yeast and scene dataset. The experimental results show that both the proposed algorithms meet or beat ML-RBF and ML-KNN when applied on the test datasets.