A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications

Wu, Di; Luo, Xin; Wang, Guoyin; Shang, Mingsheng; Yuan, Ye; Yan, Hao

doi:10.1109/tii.2017.2737827

Cited by 82 publications

(30 citation statements)

References 41 publications

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“…For the conventional neighbor-based CF methods, all the previously rated items are considered to compute the similarity between uses/items, it is time-consuming, and the methods are fail to achieve good scalability [18]. In order to reduce the computational complexity and improve the efficiency of recommendation without reducing the recommendation accuracy, subsequent clustering-based and classification-based techniques are introduced into RS to generate and evolve a variety of model-based recommendation algorithms, such as clustering models [29][30][31][32][33], singular value decomposition-(SVD-) based models [15,18], and probabilistic matrix factorization-(PMF-) based models [34][35][36][37]. Model-based methods initially train a model based on training data to find patterns and then makes predictions for real data [38,39].…”

Section: Model-based Cf Recommendation Technologymentioning

confidence: 99%

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A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests

Chen

Hua

Gao

et al. 2018

Mathematical Problems in Engineering

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Recommender systems are recently becoming more significant in the age of rapid development of the information technology and pervasive computing to provide e-commerce users’ appropriate items. In recent years, various model-based and neighbor-based approaches have been proposed, which improve the accuracy of recommendation to some extent. However, these approaches are less accurate than expected when users’ ratings on items are very sparse in comparison with the huge number of users and items in the user-item rating matrix. Data sparsity and high dimensionality in recommender systems have negatively affected the performance of recommendation. To solve these problems, we propose a hybrid recommendation approach and framework using Gaussian mixture model and matrix factorization technology. Specifically, the improved cosine similarity formula is first used to get users’ neighbors, and initial ratings on unrated items are predicted. Second, users’ ratings on items are converted into users’ preferences on items’ attributes to reduce the problem of data sparsity. Again, the obtained user-item-attribute preference data is trained through the Gaussian mixture model to classify users with the same interests into the same group. Finally, an enhanced social matrix factorization method fusing user’s and item’s social relationships is proposed to predict the other unseen ratings. Extensive experiments on two real-world datasets are conducted and the results are compared with the existing major recommendation models. Experimental results demonstrate that the proposed method achieves the better performance compared to other techniques in accuracy.

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Section: Model-based Cf Recommendation Technologymentioning

confidence: 99%

“…where represents the general name of all parameters. Loglikelihood function is described as follows (see (32)):…”

Section: Predict the Partial Ratings Using Gaussian Mixturementioning

confidence: 99%

A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests

Chen

Hua

Gao

et al. 2018

Mathematical Problems in Engineering

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“…Machine learning needs datasets recorded from field measurements or lab experiments. Although collecting data in industrial processes is not difficult as condition data are continuously monitored, labeling data samples is a time-consuming and cost-prohibitive task, and requires the expert intervention [13] [14]. Large quantities of labeled samples are required for supervised learning and deep learning.…”

Section: Introductionmentioning

confidence: 99%

“…Large quantities of labeled samples are required for supervised learning and deep learning. In this regard, semi-supervised learning (SSL) has advantages because only a small amount of labeled data are required to train a classification model from a vast amount of unlabeled data [13] [14].…”

Section: Introductionmentioning

confidence: 99%

“…SSL techniques have been employed to improve efficiency and accuracy of different fault diagnostic systems [14]- [17]. A differential evolution-based positioning optimization algorithm is proposed in [14] to label unlabeled data by a repetitive self-labeling process. Modified kernel semisupervised locally linear embedding is proposed in [17] for electrical fused magnesia furnace fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Greedy-Gradient Max Cut-Based Fault Diagnosis for Direct Online Induction Motors

2020

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In this paper, a graph-based semi-supervised learning (GSSL) algorithm, greedy-gradient max cut (GGMC), based fault diagnosis method for direct online induction motors is proposed. Two identical 0.25 HP three-phase squirrel-cage induction motors under healthy, single-and multi-fault conditions were tested in the lab. Three-phase stator currents and three-dimensional vibration signals of the two motors were recorded simultaneously in each test, and used as datasets in this study. Features for machine learning are extracted from experimental stator currents and vibration data by the discrete wavelet transform (DWT). To validate the effectiveness of the proposed GGMC-based fault diagnosis method, its classification accuracy using binary classification and multiclass classification for faults of the two motors are compared with other two GSSL algorithms, local and global consistency (LGC) and Gaussian field and harmonic function (GFHF). In this study, the performance of stator currents and vibration as a monitoring signal is evaluated, it is found that stator currents perform much better than vibration signals for multiclass classification, while they both perform well for binary classification.

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The Machine Learning Life Cycle in Chemical Operations – Status and Open Challenges

Gärtler

Khaydarov

Klöpper

et al. 2021

Chemie Ingenieur Technik

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Artificial intelligence (AI) has received a lot of attention with many publications in recent years. Interestingly related projects in the industry are mostly still in their early stages. We are convinced that progress will only be possible if the entire machine learning (ML) life cycle is considered. Our study focuses on the practical challenges, uses a recent study as foundation and adopts the life-cycle description, highlights the life-cycle practices in other domains and formulates research directions that can help to improve the utilization of AI and machine learning in the process industry.

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A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications

Cited by 82 publications

References 41 publications

A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests

A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests

Greedy-Gradient Max Cut-Based Fault Diagnosis for Direct Online Induction Motors

The Machine Learning Life Cycle in Chemical Operations – Status and Open Challenges

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