Deep Weighted Extreme Learning Machine

Size-Controllable Eu-MOFs through Machine Learning Technology: Application for High Sensitive Ions and Small-Molecular Identification

Metal-organic frameworks (MOFs) with the aggregation-induced emission (AIE) activities exhibit potential applications in the fields of energy and biomedical technology. However, the controllable synthesis of MOFs in the varied particle sizes not only affects their AIE activities, but also restricts their application scenarios. In this work, the varied particle sizes of Eu-MOFs are synthesized by adjusting the synthesis process parameters, and their variation rules combining the single factor analysis method with machine learning technology are studied. Based on the R² score, the gradient boosting decision tree (GBDT) regression model (0.9535) is employed to calculate the weight and correlation between different synthesis process parameters and it is shown that all these parameters have synergic effects on the particle sizes of Eu-MOFs, and the Eu-precursors concentration dominates in their synthesis process. Furthermore, it is indicated that the large size of Eu-MOFs and strong structural stability contribute to their high AIE activities. Finally, a screen-printed pattern is fabricated using the sample of "120-0.3-6," and this pattern exhibits a bright red fluorescence under the UV light. More importantly, this kind of Eu-MOFs can also be used to identify varied ions (Fe³⁺ , F^- , I^- , SO₄^2- , CO₃^2- , and PO₄^3- ) and citric acid.

Hierarchical One-Class Classifier With Within-Class Scatter-Based Autoencoders

Autoencoding is a vital branch of representation learning in deep neural networks (DNNs). The extreme learning machine-based autoencoder (ELM-AE) has been recently developed and has gained popularity for its fast learning speed and ease of implementation. However, the ELM-AE uses random hidden node parameters without tuning, which may generate meaningless encoded features. In this brief, we first propose a within-class scatter information constraint-based AE (WSI-AE) that minimizes both the reconstruction error and the within-class scatter of the encoded features. We then build stacked WSI-AEs into a one-class classification (OCC) algorithm based on the hierarchical regularized least-squared method. The effectiveness of our approach was experimentally demonstrated in comparisons with several state-of-the-art AEs and OCC algorithms. The evaluations were performed on several benchmark data sets.

Accurate and efficient sequential ensemble learning for highly imbalanced multi-class data

Multi-class classification for highly imbalanced data is a challenging task in which multiple issues must be resolved simultaneously, including (i) accuracy on classifying highly imbalanced multi-class data; (ii) training efficiency for large data; and (iii) sensitivity to high imbalance ratio (IR). In this paper, a novel sequential ensemble learning (SEL) framework is designed to simultaneously resolve these issues. SEL framework provides a significant property over traditional AdaBoost, in which the majority samples can be divided into multiple small and disjoint subsets for training multiple weak learners without compromising accuracy (while AdaBoost cannot). To ensure the class balance and majority-disjoint property of subsets, a learning strategy called balanced and majority-disjoint subsets division (BMSD) is developed. Unfortunately it is difficult to derive a general learner combination method (LCM) for any kind of weak learner. In this work, LCM is specifically designed for extreme learning machine, called LCM-ELM. The proposed SEL framework with BMSD and LCM-ELM has been compared with state-of-the-art methods over 16 benchmark datasets. In the experiments, under highly imbalanced multi-class data (IR up to 14K; data size up to 493K), (i) the proposed works improve the performance in different measures including G-mean, macro-F, micro-F, MAUC; (ii) training time is significantly reduced.

Regularized correntropy criterion based semi-supervised ELM

Along with the explosive growing of data, semi-supervised learning attracts increasing attention in the past years due to its powerful capability in labeling unlabeled data and knowledge mining. As an emerging method, the semi-supervised extreme learning machine (SSELM), that builds on ELM, has been developed for data classification and shown superiorities in learning efficiency and accuracy. However, the optimization of SSELM as well as most of the other ELMs is generally based on the mean square error (MSE) criterion, which has been shown less effective in dealing with non-Gaussian noises. In this paper, a robust regularized correntropy criterion based SSELM (RC-SSELM) is developed. The optimization of the output weight matrix of RC-SSELM is derived by the fixed-point iteration based approach. A convergent analysis of the proposed RC-SSELM is presented based on the half-quadratic optimization technique. Experimental results on 4 synthetic datasets and 13 benchmark UCI datasets are provided to show the superiorities of the proposed RC-SSELM over SSELM and other state-of-the-art methods.