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When performing classification tasks on high-dimensional data, traditional machine learning algorithms often fail to filter out valid information in the features adequately, leading to low levels of classification accuracy. Therefore, this paper explores the high-dimensional data from both the data feature dimension and the model ensemble dimension. We propose a high-dimensional ensemble learning classification algorithm focusing on feature space reconstruction and classifier ensemble, called the HDELC algorithm. First, the algorithm considers feature space reconstruction and then generates a feature space reconstruction matrix. It effectively achieves feature selection and reconstruction for high-dimensional data. An optimal feature space is generated for the subsequent ensemble of the classifier, which enhances the representativeness of the feature space. Second, we recursively determine the number of classifiers and the number of feature subspaces in the ensemble model. Different classifiers in the ensemble system are assigned mutually exclusive non-intersecting feature subspaces for model training. The experimental results show that the HDELC algorithm has advantages compared with most high-dimensional datasets due to its more efficient feature space ensemble capability and relatively reliable ensemble operation performance. The HDELC algorithm makes it possible to solve the classification problem for high-dimensional data effectively and has vital research and application value. [ABSTRACT FROM AUTHOR]

As technology advances, the equipment becomes more complicated, and the importance of the Prognostics and Health Management (PHM) to monitor the condition of the equipment has risen. In recent years, various methodologies have emerged. With the development of computing technology, methodologies using machine learning and deep learning are gaining attention, in particular. As these algorithms become more advanced, the performance of detecting anomalies and predicting failures has improved dramatically. However, most of the studies are cases that depend on simulation data or assumed abnormal conditions. In addition, regardless of the existence of run-to-failure data, the methodologies are difficult to apply to the industrial site directly. To solve this problem, we propose a Predictive Maintenance (PdM) framework based on unsupervised learning in this paper, which can be applied directly in the industrial field regardless of run-to-failure data. The proposed framework consists of data acquisition, preprocessing data, constructing a Health Index, and predicting the remaining useful life. We propose a framework that can create and monitor models even when there are no accumulated run-to-failure data. The proposed framework was conducted in two different real-life cases, and the usefulness and applicability of the proposed methodology were verified. [ABSTRACT FROM AUTHOR]

Community detection is an important task in the analysis of complex networks, which is significant for mining and analyzing the organization and function of networks. As an unsupervised learning algorithm based on the particle competition mechanism, stochastic competitive learning has been applied in the field of community detection in complex networks, but still has several limitations. In order to improve the stability and accuracy of stochastic competitive learning and solve the problem of community detection, we propose an unsupervised community detection algorithm LNSSCL (Local Node Similarity-Integrated Stochastic Competitive Learning). The algorithm calculates node degree as well as Salton similarity metrics to determine the starting position of particle walk; local node similarity is incorporated into the particle preferential walk rule; the particle is dynamically adjusted to control capability increments according to the control range; particles select the node with the strongest control capability within the node to be resurrected; and the LNSSCL algorithm introduces a node affiliation selection step to adjust the node community labels. Experimental comparisons with 12 representative community detection algorithms on real network datasets and synthetic networks show that the LNSSCL algorithm is overall better than other compared algorithms in terms of standardized mutual information (NMI) and modularity (Q). The improvement effect for the stochastic competition learning algorithm is evident, and it can effectively accomplish the community detection task in complex networks. [ABSTRACT FROM AUTHOR]

Nowadays, most fuzzy clustering algorithms are sensitive to the initialization results of clustering algorithms and have a weak ability to handle high-dimensional data. To solve these problems, we developed the viewpoint-driven subspace fuzzy c-means (VSFCM) algorithm. Firstly, we propose a new cut-off distance. Based on this, we establish the cut-off distance-induced clustering initialization (CDCI) method and use it as a new strategy for cluster center initialization and viewpoint selection. Secondly, by taking the viewpoint obtained by CDCI as the entry point of knowledge, a new fuzzy clustering strategy driven by knowledge and data is formed. Based upon these, we put forward the VSFCM algorithm combined with viewpoints, separation terms, and subspace fuzzy feature weights. Moreover, compared with the symmetric weights obtained by other subspace clustering algorithms, the weights of the VSFCM algorithm exhibit significant asymmetry. That is, they assign greater weights to features that contribute more, which is validated on the artificial dataset DATA2 in the experimental section. The experimental results compared with multiple advanced clustering algorithms on the three types of datasets validate that the proposed VSFCM algorithm has the best performance in five indicators. It is demonstrated that the initialization method CDCI is more effective, the feature weight allocation of VSFCM is more consistent with the asymmetry of experimental data, and it can achieve better convergence speed while displaying better clustering efficiency. [ABSTRACT FROM AUTHOR]

A social tagging system improves recommendation performance by introducing tags as auxiliary information. These tags are text descriptions of target items provided by individual users, which can be arbitrary words or phrases, so they can provide more abundant information about user interests and item characteristics. However, there are many problems to be solved in tag information, such as data sparsity, ambiguity, and redundancy. In addition, it is difficult to capture multi-aspect user interests and item characteristics from these tags, which is essential to the recommendation performance. In the view of these situations, we propose a tag-aware recommendation model based on attention learning, which can capture diverse tag-based potential features for users and items. The proposed model adopts the embedding method to produce dense tag-based feature vectors for each user and each item. To compress these vectors into a fixed-length feature vector, we construct an attention pooling layer that can automatically allocate different weights to different features according to their importance. We concatenate the feature vectors of users and items as the input of a multi-layer fully connected network to learn non-linear high-level interaction features. In addition, a generalized linear model is also conducted to extract low-level interaction features. By integrating these features of different types, the proposed model can provide more accurate recommendations. We establish extensive experiments on two real-world datasets to validate the effect of the proposed model. Comparable results show that our model perform better than several state-of-the-art tag-aware recommendation methods in terms of HR and NDCG metrics. Further ablation studies also demonstrate the effectiveness of attention learning. [ABSTRACT FROM AUTHOR]