Improved one-class classification using filled function

Hamidzadeh, Javad; Moradi, Mona

doi:10.1007/s10489-018-1145-y

Cited by 17 publications

(5 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, artificial neural network (ANN) is a nonlinear mapping and adaptive dynamic technique formed by several simple neurons widely interconnected [30]. Added to that, back propagation (BP) and radial basis function (RBF) networks have the disadvantages of slow learning speed, being easy to fall into local extremes, and having low accuracy of prediction results.…”

Section: Research On Neural Network Modelsmentioning

confidence: 99%

Prediction of the Health Status of Older Adults Using Oversampling and Neural Network

Li,

Hu,

Xie

et al. 2023

Mathematics

View full text Add to dashboard Cite

Self-rated health (SRH) serves as an important indicator for measuring the physical and mental well-being of older adults, holding significance for their health management and disease prevention. In this paper, we introduce a novel classification method based on oversampling and neural network with the objective of enhancing the accuracy of predict the SRH of older adults. Utilizing data from the 2020 China Family Panel Studies (CFPS), we included a total of 6596 participants aged 60 years and above in our analysis. To mitigate the impact of imbalanced data, an improved oversampling was proposed, known as weighted Tomek-links adaptive semi-unsupervised weighted oversampling (WTASUWO). It firstly removes the features that are not relevant to the classification by ReliefF. Consequently, it combines undersampling and oversampling. To improve the prediction accuracy of the classifier, an improved multi-layer perception (IMLP) for predicting the SRH was constructed based on bagging and adjusted learning rate. Referring to the experimental results, WTASUWO can effectively improve the prediction performance of a classifier when being applied on an imbalanced dataset, and the IMLP using WTASUWO achieves a higher accuracy. This method can more objectively and accurately assess the health status and identify factors affecting the SRH of older adults. By mining relevant information related the health status of older adults and constructing the prediction model, we can provide policymakers and healthcare professionals with targeted intervention techniques to focus on the health needs of older adults. Meanwhile, this method provides a practical research basis for improving the health level of older adults in China.

show abstract

Section: Research On Neural Network Modelsmentioning

confidence: 99%

Prediction of the Health Status of Older Adults Using Oversampling and Neural Network

Li,

Hu,

Xie

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…The term OCC was first introduced by [9] to denote a category of classification algorithms that address cases where few to none defect samples are available for training; the normal class is well-defined while abnormal one is under-sampled [10] which is quite common in industrial areas [11] ,and with that, defects are seen as a deviation from defect-free class. The OCC concept encompasses several approaches, such as methods based on density [12], distance [13], neural networks [14], [15], and boundary approaches [16] that aims to encircle normal samples by a decision boundary. The work [17] developed adversarially trained deep neural networks, the first component works as image reconstructor, while the second represents the classifier.…”

Section: Introductionmentioning

confidence: 99%

“…According to [20], replacing the inner product 〈z 𝒾 ′ , z 𝒿 ′ 〉 in (12) with appropriate kernel function 𝐾(𝓏 𝒾 ′ , 𝓏 𝒿 ′ )provides more flexibility to define the boundary. We have chosen to use the Gaussian kernel function, given that it is the kernel reported to be yielding satisfactory results in OCC applications [29].…”

mentioning

confidence: 99%

Product defect detection based on convolutional autoencoder and one-class classification

Chaabi¹,

Hamlich²,

Garouani³

2023

IJ-AI

View full text Add to dashboard Cite

<span lang="EN-US">To meet customer expectations and remain competitive, industrials try constantly to improve their quality control systems. There is hence increasing demand for adopting automatic defect detection solutions. However, the biggest issue in addressing such systems is the imbalanced aspect of industrial datasets. Often, defect-free samples far exceed the defected ones, due to continuous improvement approaches adopted by manufacturing companies. In this sense, we propose an automatic defect detection system based on one-class classification (OCC) since it involves only normal samples during training. It consists of three sub-models, first, a convolutional autoencoder serves as latent features extractor, the extracted features vectors are subsequently fed into the dimensionality reduction process by performing principal component analysis (PCA), then the reduced-dimensional data are used to train the one-class classifier support vector data description (SVDD). During the test phase, both normal and defected images are used. The first two stages of the trained model generate a low-dimensional features vector, whereas the SVDD classifies the new input, whether it is defect-free or defected. This approach is evaluated on the carpet images from the industrial inspection dataset MVTec anomaly detection (MVTec AD). During training, only normal images were used. The results showed that the proposed method outperforms the state-of-the-art methods.</span>

show abstract

“…One-class Classification (OCC) has been widely used for outlier, novelty, fault, and intrusion detection [1][2][3][4][5][6] by researchers from different disciplines. In multiclass problem, both positive and negative samples are available for training [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…where C is a regularization parameter, and e h i is a training error vector corresponding to the i th training sample at h th layer. Based on the minimization criterion in (5), LKAE can be formulated as follows:…”

Section: Introductionmentioning

confidence: 99%

Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)

Gautam¹,

Tiwari

Tanveer

2019

Preprint

View full text Add to dashboard Cite

show abstract

Improved one-class classification using filled function

Cited by 17 publications

References 43 publications

Prediction of the Health Status of Older Adults Using Oversampling and Neural Network

Prediction of the Health Status of Older Adults Using Oversampling and Neural Network

Product defect detection based on convolutional autoencoder and one-class classification

Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)

Contact Info

Product

Resources

About