Ensemble Regression Kernel Extreme Learning Machines for Multi-Instance Multi-Label Learning

The multi-instance multi-label learning (MIML) framework is an extension of the multi-label learning, where each object in MIML is represented by a multi-instance bag and associated with a multi-label vector. Recently, extreme learning machine (ELM) has been widely used in multi-instance multi-label classification due to its short runtime. Simultaneously, ELM also has good classification accuracy compared to other neural network models. However, this type of ELM-based MIML classification algorithms can easily lead to overfitting problems during training and the basic ELM algorithm with random initial weights and biases is not stable. In order to solve the above problems, ensemble learning is used to overcome overfitting problems and regression kernel extreme learning machine (RKELM) as classifier instead of the basic ELM effectively can solve the problem of instability of training. In this paper, Bagging-based RKELM (BRKELM) and AdaBoost-based RKELM (ARKELM) for MIML classifications are proposed. The comparison with other state-of-the-art multi-instance multi-label learning algorithms shows that the BRKELM and ARKELM are highly efficient, feasible and stable algorithms.

[1]  Zhi-Hua Zhou,et al.  Towards Discovering What Patterns Trigger What Labels , 2012, AAAI.

[2]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[3]  Wenyong Zhu,et al.  Predicting Protein Functions of Bacteria Genomes via Multi-instance Multi-Label Active Learning , 2018, 2018 IEEE 3rd International Conference on Integrated Circuits and Microsystems (ICICM).

[4]  Min-Ling Zhang,et al.  A k-Nearest Neighbor Based Multi-Instance Multi-Label Learning Algorithm , 2010, 2010 22nd IEEE International Conference on Tools with Artificial Intelligence.

[5]  Erkki Oja,et al.  GPU-accelerated and parallelized ELM ensembles for large-scale regression , 2011, Neurocomputing.

[6]  Min-Ling Zhang,et al.  A Review on Multi-Label Learning Algorithms , 2014, IEEE Transactions on Knowledge and Data Engineering.

[7]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[8]  Haifeng Guo,et al.  Deep Multi-Instance Multi-Label Learning for Image Annotation , 2018, Int. J. Pattern Recognit. Artif. Intell..

[9]  Han Wang,et al.  Ensemble Based Extreme Learning Machine , 2010, IEEE Signal Processing Letters.

[10]  Linda G. Shapiro,et al.  Multi-Instance Multi-Label Learning for Multi-Class Classification of Whole Slide Breast Histopathology Images , 2018, IEEE Transactions on Medical Imaging.

[11]  Zhi-Hua Zhou,et al.  Multi-Instance Multi-Label Learning with Application to Scene Classification , 2006, NIPS.

[12]  Pawel Teisseyre,et al.  CCnet: Joint multi-label classification and feature selection using classifier chains and elastic net regularization , 2017, Neurocomputing.

[13]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[14]  Bin Zhang,et al.  Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine , 2016 .

[15]  Xiaoli Z. Fern,et al.  Rank-loss support instance machines for MIML instance annotation , 2012, KDD.

[16]  Zhi-Hua Zhou,et al.  Fast Multi-Instance Multi-Label Learning , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Xindong Wu,et al.  Multi-Instance Learning with Discriminative Bag Mapping , 2018, IEEE Transactions on Knowledge and Data Engineering.

[18]  Canlong Zhang,et al.  A New multi-instance multi-label learning approach for image and text classification , 2016, Multimedia Tools and Applications.

[19]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[20]  Guang-Bin Huang,et al.  Trends in extreme learning machines: A review , 2015, Neural Networks.

[21]  Jun Li,et al.  ${{\rm E}^{2}}{\rm LMs}$ : Ensemble Extreme Learning Machines for Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[22]  Zhi-Hua Zhou,et al.  Genome-Wide Protein Function Prediction through Multi-Instance Multi-Label Learning , 2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[23]  Zhi-Hua Zhou,et al.  Multi-instance multi-label learning , 2008, Artif. Intell..