scholarly journals Sparse logistic regression for whole-brain classification of fMRI data

NeuroImage ◽  
2010 ◽  
Vol 51 (2) ◽  
pp. 752-764 ◽  
Author(s):  
Srikanth Ryali ◽  
Kaustubh Supekar ◽  
Daniel A. Abrams ◽  
Vinod Menon
Keyword(s):  
Logistic Regression ◽  
Fmri Data ◽  
Whole Brain ◽  
Sparse Logistic Regression

scholarly journals Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization

2018 ◽  
Vol 8 (9) ◽  
pp. 1569 ◽  
Author(s):  
Shengbing Wu ◽  
Hongkun Jiang ◽  
Haiwei Shen ◽  
Ziyi Yang
Keyword(s):  
Logistic Regression ◽  
Gene Selection ◽  
Classification Performance ◽  
Cancer Classification ◽  
Sparse Logistic Regression ◽  
The Subject ◽  
Selection For ◽  
Microarray Datasets ◽  
Sparse Methods

In recent years, gene selection for cancer classification based on the expression of a small number of gene biomarkers has been the subject of much research in genetics and molecular biology. The successful identification of gene biomarkers will help in the classification of different types of cancer and improve the prediction accuracy. Recently, regularized logistic regression using the L 1 regularization has been successfully applied in high-dimensional cancer classification to tackle both the estimation of gene coefficients and the simultaneous performance of gene selection. However, the L 1 has a biased gene selection and dose not have the oracle property. To address these problems, we investigate L 1 / 2 regularized logistic regression for gene selection in cancer classification. Experimental results on three DNA microarray datasets demonstrate that our proposed method outperforms other commonly used sparse methods ( L 1 and L E N ) in terms of classification performance.

scholarly journals Classification of breast lesions in ultrasonography using sparse logistic regression and morphology-based texture features

2018 ◽  
Vol 45 (9) ◽  
pp. 4112-4124 ◽  
Author(s):  
Hoda Nemat ◽  
Hamid Fehri ◽  
Nasrin Ahmadinejad ◽  
Alejandro F. Frangi ◽  
Ali Gooya
Keyword(s):  
Logistic Regression ◽  
Texture Features ◽  
Breast Lesions ◽  
Sparse Logistic Regression

scholarly journals Classification of mislabelled microarrays using robust sparse logistic regression

2013 ◽  
Vol 29 (7) ◽  
pp. 870-877 ◽  
Author(s):  
Jakramate Bootkrajang ◽  
Ata Kabán
Keyword(s):  
Logistic Regression ◽  
Sparse Logistic Regression

Euler Elastica Regularized Logistic Regression for Whole-Brain Decoding of fMRI Data

2018 ◽  
Vol 65 (7) ◽  
pp. 1639-1653 ◽  
Author(s):  
Chuncheng Zhang ◽  
Li Yao ◽  
Sutao Song ◽  
Xiaotong Wen ◽  
Xiaojie Zhao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Fmri Data ◽  
Whole Brain ◽  
Brain Decoding ◽  
Euler Elastica

scholarly journals An Additive Sparse Logistic Regularization Method for Cancer Classification in Microarray Data

2021 ◽  
Vol 18 (2) ◽  
Keyword(s):  
Logistic Regression ◽  
Microarray Data ◽  
High Dimensional ◽  
Cancer Data ◽  
Abnormal Growth ◽  
Sparse Logistic Regression ◽  
Genome Data ◽  
Regularization Techniques ◽  
Performance Of Algorithm

Now a day’s cancer has become a deathly disease due to the abnormal growth of the cell. Many researchers are working in this area for the early prediction of cancer. For the proper classification of cancer data, demands for the identification of proper set of genes by analyzing the genomic data. Most of the researchers used microarrays to identify the cancerous genomes. However, such kind of data is high dimensional where number of genes are more compared to samples. Also the data consists of many irrelevant features and noisy data. The classification technique deal with such kind of data influences the performance of algorithm. A popular classification algorithm (i.e., Logistic Regression) is considered in this work for gene classification. Regularization techniques like Lasso with L1 penalty, Ridge with L2 penalty, and hybrid Lasso with L1/2+2 penalty used to minimize irrelevant features and avoid overfitting. However, these methods are of sparse parametric and limits to linear data. Also methods have not produced promising performance when applied to high dimensional genome data. For solving these problems, this paper presents an Additive Sparse Logistic Regression with Additive Regularization (ASLR) method to discriminate linear and non-linear variables in gene classification. The results depicted that the proposed method proved to be the best-regularized method for classifying microarray data compared to standard methods

Classification of ASD based on fMRI data with deep learning

2021 ◽  
Author(s):  
Lizhen Shao ◽  
Cong Fu ◽  
Yang You ◽  
Dongmei Fu
Keyword(s):  
Deep Learning ◽  
Fmri Data
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