Optimal combination of feature selection and classification via local hyperplane based learning strategy

Cheng, Xiaoping; Cai, Hongming; Zhang, Yue; Xu, Bo; Su, Weifeng

doi:10.1186/s12859-015-0629-6

Cited by 13 publications

(9 citation statements)

References 22 publications

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“…Gene expression datasets may have very high dimensionality. High-dimensional gene expression datasets always result in high computational load and degradation of model performance of classification algorithm [ 10 , 19 , 21 ]. Therefore, feature selection approach or dimensionality reduction methods should eliminate redundant and irrelevant feature data to decrease the ratio of features to samples.…”

Section: Methodsmentioning

confidence: 99%

“…Another issue is that the dimension of the feature space may be too high for classification algorithm. Numerous algorithms may become invalid or infeasible when lots of feature data in a dimension have to be processed [ 19 – 21 ]. A successful method is to extract a small number of discriminative information from a high-dimensional space.…”

Section: Introductionmentioning

confidence: 99%

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Collaborative representation-based classification of microarray gene expression data

Shen

Jiang

et al. 2017

PLoS ONE

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Microarray technology is important to simultaneously express multiple genes over a number of time points. Multiple classifier models, such as sparse representation (SR)-based method, have been developed to classify microarray gene expression data. These methods allocate the gene data points to different clusters. In this paper, we propose a novel collaborative representation (CR)-based classification with regularized least square to classify gene data. First, the CR codes a testing sample as a sparse linear combination of all training samples and then classifies the testing sample by evaluating which class leads to the minimum representation error. This CR-based classification approach is remarkably less complex than traditional classification methods but leads to very competitive classification results. In addition, compressive sensing approach is adopted to project the high-dimensional gene expression dataset to a lower-dimensional space which nearly contains the whole information. This compression without loss is beneficial to reduce the computational load. Experiments to detect subtypes of diseases, such as leukemia and autism spectrum disorders, are performed by analyzing the gene expression. The results show that the proposed CR-based algorithm exhibits significantly higher stability and accuracy than the traditional classifiers, such as support vector machine algorithm.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Collaborative representation-based classification of microarray gene expression data

Shen

Jiang

et al. 2017

PLoS ONE

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“…The parallelization of weighted voting combined prediction method is to divide the data set into N data blocks [3]. The data blocks of each processing node train the corresponding weighted voting combined prediction model, and the training results of each part are summarized as the training set of the aggregate prediction model to train the model [4].…”

Section: Data Partitioningmap() and Reduce()mentioning

confidence: 99%

Prediction Model Hadoop-based for High-risk Students

Yu¹,

Yu²

2019

Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMS

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In order to effectively predict high-risk students, this paper proposed a weighted voting combination prediction model based on Hadoop for high-risk students. Firstly, the design idea of the model was given, and the data of students were stored and processed by Hadoop platform. Secondly, according to the prediction method of students' characteristics and selection, a specific weighted voting combination prediction model Hadoopbased for high-risk students was constructed. Finally, the prediction model was evaluated and the rationality of the model was proved. The model can analyze the collected data of students and predicts the high-risk students in big data environment.

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“…On the one hand, CXR imaging is inexpensive, swift and universal but unable to detect acute pneumonia in previous studies [4,6,28]. On the other hand, CT imaging is now clinically adoptable as the principal way to confirm positive or suspected-positive COVID-19 cases [11].…”

Section: Introductionmentioning

confidence: 99%

Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort

Zhu

Zhang

Tao

et al. 2021

Interdiscip Sci Comput Life Sci

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Corona Virus Disease (COVID-19) has spread globally quickly, and has resulted in a large number of causalities and medical resources insufficiency in many countries. Reverse-transcriptase polymerase chain reaction (RT-PCR) testing is adopted as biopsy tool for confirmation of virus infection. However, its accuracy is as low as 60-70%, which is inefficient to uncover the infected. In comparison, the chest CT has been considered as the prior choice in diagnosis and monitoring progress of COVID-19 infection. Although the COVID-19 diagnostic systems based on artificial intelligence have been developed for assisting doctors in diagnosis, the small sample size and the excessive time consumption limit their applications. To this end, this paper proposed a diagnosis prototype system for COVID-19 infection testing. The proposed deep learning model is trained and is tested on 2267 CT sequences from 1357 patients clinically confirmed with COVID-19 and 1235 CT sequences from non-infected people. The main highlights of the prototype system are: (1) no data augmentation is needed to accurately discriminate the COVID-19 from normal controls with the specificity of 0.92 and sensitivity of 0.93; (2) the raw DICOM image is not necessary in testing. Highly compressed image like Jpeg can be used to allow a quick diagnosis; and (3) it discriminates the virus infection within 6 seconds and thus allows an online test with light cost. We also applied our model on 48 asymptomatic patients diagnosed with COVID-19. We found that: (1) the positive rate of RT-PCR assay is 63.5% (687/1082). (2) 45.8% (22/48) of the RT-PCR assay is negative for asymptomatic patients, yet the accuracy of CT scans is 95.8%. The online detection system is available: http://212.64.70.65/covid. Ziwei Zhu, Guihua Tao and Tingting Dan have contributed equally to this study and are considered as joint first authors.

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Optimal combination of feature selection and classification via local hyperplane based learning strategy

Cited by 13 publications

References 22 publications

Collaborative representation-based classification of microarray gene expression data

Collaborative representation-based classification of microarray gene expression data

Prediction Model Hadoop-based for High-risk Students

Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort

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