Gene selection in cancer classification using sparse logistic regression with Bayesian regularization

Cawley, Gavin C.; Talbot, Nicola L. C.

doi:10.1093/bioinformatics/btl386

Cited by 226 publications

(147 citation statements)

References 15 publications

Supporting

Mentioning

147

Contrasting

Order By: Relevance

“…This classifier has been successfully used with high dimensional data (gene selection in cancer classification [133], feature selection in remote sensing [28,29,134]). …”

Section: Regularized Logistic Regression (Rlr)mentioning

confidence: 99%

Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements

et al. 2017

View full text Add to dashboard Cite

This study aims to evaluate three classes of methods to discriminate between 13 peatland vegetation types using reflectance data. These vegetation types were empirically defined according to their composition, strata and biodiversity richness. On one hand, it is assumed that the same vegetation type spectral signatures have similarities. Consequently, they can be compared to a reference spectral database. To catch those similarities, several similarities criteria (related to distances (Euclidean distance, Manhattan distance, Canberra distance) or spectral shapes (Spectral Angle Mapper) or probabilistic behaviour (Spectral Information Divergence)) and several mathematical transformations of spectral signatures enhancing absorption features (such as the first derivative or the second derivative, the normalized spectral signature, the continuum removal, the continuum removal derivative reflectance, the log transformation) were investigated. Furthermore, those similarity measures were applied on spectral ranges which characterize specific biophysical properties. On the other hand, we suppose that specific biophysical properties/components may help to discriminate between vegetation types applying supervised classification such as Random Forest (RF), Support Vector Machines (SVM), Regularized Logistic Regression (RLR), Partial Least Squares-Discriminant Analysis (PLS-DA). Biophysical components can be used in a local way considering vegetation spectral indices or in a global way considering spectral ranges and transformed spectral signatures, as explained above. RLR classifier applied on spectral vegetation indices (training size = 25%) was able to achieve 77.21% overall accuracy in discriminating peatland vegetation types. It was also able to discriminate between 83.95% vegetation types considering specific spectral range [350-1350 nm], first derivative of spectral signatures and training size = 25%. Conversely, similarity criterion was able to achieve 81.70% overall accuracy using the Canberra distance computed on the full spectral range [350-2500 nm]. The results of this study suggest that RLR classifier and similarity criteria are promising to map the different vegetation types with high ecological values despite vegetation heterogeneity and mixture.

show abstract

“…This classifier has been successfully used with high dimensional data (gene selection in cancer classification [133], feature selection in remote sensing [28,29,134]). …”

Section: Regularized Logistic Regression (Rlr)mentioning

confidence: 99%

Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements

et al. 2017

View full text Add to dashboard Cite

show abstract

“…However, these methods are usually computationally expensive [14] and may not be able to be applied on large scale data mining problems. For embedded model, the procedure of feature selection is embedded directly in the training process [2,3].…”

Section: Related Workmentioning

confidence: 99%

Feature Selection with Linked Data in Social Media

Tang

Liu

2012

Proceedings of the 2012 SIAM International Conference on Data Mining

102

View full text Add to dashboard Cite

Feature selection is widely used in preparing highdimensional data for effective data mining. Increasingly popular social media data presents new challenges to feature selection. Social media data consists of (1) traditional high-dimensional, attribute-value data such as posts, tweets, comments, and images, and (2) linked data that describes the relationships between social media users as well as who post the posts, etc. The nature of social media also determines that its data is massive, noisy, and incomplete, which exacerbates the already challenging problem of feature selection. In this paper, we illustrate the differences between attributevalue data and social media data, investigate if linked data can be exploited in a new feature selection framework by taking advantage of social science theories, extensively evaluate the effects of user-user and user-post relationships manifested in linked data on feature selection, and discuss some research issues for future work.

show abstract

“…For example, Shevade and Keerthi (2003) proposed the sparse logistic regression based on the LASSO penalty. Similar to sparse logistic regression with the LASSO penalty, Cawley and Talbot (2006) investigated sparse logistic regression with Bayesian penalty. Liang et al (2013) did another investigation in the sparse logistic regression model using a 1 2 penalty.…”

Section: Methodology Penalized Logistic Regression Methodsmentioning

confidence: 99%

Applying Penalized Binary Logistic Regression with Correlation Based Elastic Net for Variables Selection

Algamal

Lee

2015

J. Mod. App. Stat. Meth.

View full text Add to dashboard Cite

Reduction of the high dimensional classification using penalized logistic regression is one of the challenges in applying binary logistic regression. The applied penalized method, correlation based elastic penalty (CBEP), was used to overcome the limitation of LASSO and elastic net in variable selection when there are perfect correlation among explanatory variables. The performance of the CBEP was demonstrated through its application in analyzing two well-known high dimensional binary classification data sets. The CBEP provided superior classification performance and variable selection compared with other existing penalized methods. It is a reliable penalized method in binary logistic regression.

show abstract

Gene selection in cancer classification using sparse logistic regression with Bayesian regularization

Abstract: A MATLAB implementation of the sparse logistic regression algorithm with Bayesian regularization (BLogReg) is available from http://theoval.cmp.uea.ac.uk/~gcc/cbl/blogreg/

Cited by 226 publications

References 15 publications

Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements

Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements

Feature Selection with Linked Data in Social Media

Applying Penalized Binary Logistic Regression with Correlation Based Elastic Net for Variables Selection

Contact Info

Product

Resources

About