Sample imbalance disease classification model based on association rule feature selection

Deep learning has the ability to mine complex relationships in fault diagnosis. Deep convolutional neural network (DCNN) with deep structures, instead of shallow ones, can be applied to mining useful information from the original vibration data. However, when the number of the training samples is small, the diagnosis accuracy will be affected. As an improvement of the DCNN, deep convolutional neural network based on the Fisher-criterion (FDCNN) can be used for the fault diagnosis of small samples. But the model parameters in the method are based on human labor or prior knowledge, which is bound to bring negative influence on the diagnosis accuracy. Therefore, a novel adaptive Fisher-based deep convolutional neural network (AFDCNN) method, which can optimize the model parameters adaptively, is proposed as an improvement of the FDCNN. Comparative verification test results show that AFDCNN has more outstanding performance.

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“…. , (x (m) , y (m) ) and they are n categories, respectively, the traditional energy function [26] can be represented as follows:…”

Section: Adaptive Fisher-based Deep Convolutionalmentioning

confidence: 99%

“…Here comes the problem, the experimental vibration samples with labels cannot be always sufficient. In which, some of them are very difficult to obtain [26,27]. e deep convolutional neural network based on the Fisher-criterion (FDCNN) is used for words recognition of small samples [28].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fisher-Based Deep Convolutional Neural Network and Its Application to Recognition of Rolling Element Bearing Fault Patterns and Sizes

Luo

Zhang

et al. 2020

Mathematical Problems in Engineering

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“…Learning from imbalanced data is an important and hot topic in machine learning, as it has been widely applied to diagnose and classify diseases [1,2], detect software defects [3,4], analyze biology and pharmacology data [5,6], evaluate credit risk [7], predict actionable revenue change and bankruptcy [8,9], diagnose faults in the industrial procedure [10,11], classify soil types [12,13], and even predict crash injury severity [14] or analyze crime linkages [15]. Meanwhile, class imbalance learning (CIL) is also a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Probability Density Machine: A New Solution of Class Imbalance Learning

Cheng

Longfei

et al. 2021

Scientific Programming

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Class imbalance learning (CIL) is an important branch of machine learning as, in general, it is difficult for classification models to learn from imbalanced data; meanwhile, skewed data distribution frequently exists in various real-world applications. In this paper, we introduce a novel solution of CIL called Probability Density Machine (PDM). First, in the context of Gaussian Naive Bayes (GNB) predictive model, we analyze the reason why imbalanced data distribution makes the performance of predictive model decline in theory and draw a conclusion regarding the impact of class imbalance that is only associated with the prior probability, but does not relate to the conditional probability of training data. Then, in such context, we show the rationality of several traditional CIL techniques. Furthermore, we indicate the drawback of combining GNB with these traditional CIL techniques. Next, profiting from the idea of K-nearest neighbors probability density estimation (KNN-PDE), we propose the PDM which is an improved GNB-based CIL algorithm. Finally, we conduct experiments on lots of class imbalance data sets, and the proposed PDM algorithm shows the promising results.

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“…e electronization, informatization, and intelligentization of hospitals can effectively improve the efficiency of the medical system. Massive electronic medical imaging data provide powerful data support for intelligent auxiliary diagnosis algorithms and also pose new challenges for multisource complex data storage and access [1][2][3][4]. In the new application scenario, doctors use electronic images to replace the traditional film for diagnosis, and patients can view the examination images at any time through various electronic methods (WeChat and AliPay).…”

Section: Introductionmentioning

confidence: 99%

Hadoop-Based Medical Image Storage and Access Method for Examination Series

Huang

Wang³

et al. 2021

Mathematical Problems in Engineering

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Under the background of electronic medical data, doctors use electronic images to replace the traditional film for diagnosis, and patients can view examination images at any time through various electronic means. The storage and frequent reading of massive data bring new challenges. Given the characteristics of the size and quantity of image files generated by different examination types, different merging strategies are proposed to improve the storage performance of the files; according to the characteristics of medical data with examination as the basic unit, a two-level model combined with medical imaging information is proposed. The indexing mechanism solves the problem that SEQ files cannot be read randomly without an index; given the time characteristics of data access, an improved 2Q algorithm is proposed to cache the prefetched files and the read files in different cache queues, which improves the efficiency of file reading. In the experimental comparison, the proposed algorithm surpasses the baseline method in storage and access performance.

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Sample imbalance disease classification model based on association rule feature selection

Cited by 53 publications

References 16 publications

Adaptive Fisher-Based Deep Convolutional Neural Network and Its Application to Recognition of Rolling Element Bearing Fault Patterns and Sizes

Adaptive Fisher-Based Deep Convolutional Neural Network and Its Application to Recognition of Rolling Element Bearing Fault Patterns and Sizes

Probability Density Machine: A New Solution of Class Imbalance Learning

Hadoop-Based Medical Image Storage and Access Method for Examination Series

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