Fused lasso for feature selection using structural information

Cui, Lixin; Bai, Lu; Wang, Yue; Yu, Philip S.; Hancock, Edwin R.

doi:10.1016/j.patcog.2021.108058

Cited by 33 publications

(7 citation statements)

References 33 publications

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“…Additionally, LASSO aids in avoiding overfitting by preventing the model from becoming excessively complex, which can generalize well to unseen data. Therefore, the LASSO method provides a powerful and efficient approach to feature selection by effectively handling highdimensional datasets, promoting interpretability, and robustness against multicollinearity, and preventing overfitting [30]. The important factors selected by LASSO involved in predicting the final math grade of students were first and second-period grades, quality of family relationships, age, number of school absences, weekend alcohol consumption, current health status, the reason for choosing the school, weekly study time, and home to school time arrival.…”

Section: Discussionmentioning

confidence: 99%

Predicting Math Performance in High School Students using Machine Learning Techniques

hui

2024

ijacsa

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Section: Discussionmentioning

confidence: 99%

Predicting Math Performance in High School Students using Machine Learning Techniques

hui

2024

ijacsa

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“…where cov(g k , g l ) is the covariance between g k and g l , σ g k is the standard deviation of g k , and σ g l is the standard deviation of g l . The use of Fused LASSO is motivated by several factors [69]. One of the main advantages is its ability to handle high-dimensional data where the number of genes exceeds the number of samples.…”

Section: Generalized Fused Lassomentioning

confidence: 99%

GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification

Bir-Jmel,

Douiri,

Bernoussi

et al. 2024

Computers

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Advancements in genomic technologies have paved the way for significant breakthroughs in cancer diagnostics, with DNA microarray technology standing at the forefront of identifying genetic expressions associated with various cancer types. Despite its potential, the vast dimensionality of microarray data presents a formidable challenge, necessitating efficient dimension reduction and gene selection methods to accurately identify cancerous tumors. In response to this challenge, this study introduces an innovative strategy for microarray data dimension reduction and crucial gene set selection, aiming to enhance the accuracy of cancerous tumor identification. Leveraging DNA microarray technology, our method focuses on pinpointing significant genes implicated in tumor development, aiding the development of sophisticated computerized diagnostic tools. Our technique synergizes gene selection with classifier training within a logistic regression framework, utilizing a generalized Fused LASSO (GFLASSO-LR) regularizer. This regularization incorporates two penalties: one for selecting pertinent genes and another for emphasizing adjacent genes of importance to the target class, thus achieving an optimal trade-off between gene relevance and redundancy. The optimization challenge posed by our approach is tackled using a sub-gradient algorithm, designed to meet specific convergence prerequisites. We establish that our algorithm’s objective function is convex, Lipschitz continuous, and possesses a global minimum, ensuring reliability in the gene selection process. A numerical evaluation of the method’s parameters further substantiates its effectiveness. Experimental outcomes affirm the GFLASSO-LR methodology’s high efficiency in processing high-dimensional microarray data for cancer classification. It effectively identifies compact gene subsets, significantly enhancing classification performance and demonstrating its potential as a powerful tool in cancer research and diagnostics.

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“…While candidate feature relevancy is considered to be equivalent to selected feature relevancy in mutual information, some less relevant features may be misinterpreted as salient features. To overcome these issues, the fusion LASSO enhances the trade-off between the relevancy of each individual feature [35]. However, due to the structured penalties, the fusion LASSO cost is computationally high when dealing with EEG dataset.…”

Section: Related Workmentioning

confidence: 99%

Efficient Feature Learning Model of Motor Imagery EEG Signals with L1-Norm and Weighted Fusion

Kong,

Wu,

Chen

et al. 2024

Biosensors

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Brain–computer interface (BCI) for motor imagery is an advanced technology used in the field of medical rehabilitation. However, due to the poor accuracy of electroencephalogram feature classification, BCI systems often misrecognize user commands. Although many state-of-the-art feature selection methods aim to enhance classification accuracy, they usually overlook the interrelationships between individual features, indirectly impacting the accuracy of feature classification. To overcome this issue, we propose an adaptive feature learning model that employs a Riemannian geometric approach to generate a feature matrix from electroencephalogram signals, serving as the model’s input. By integrating the enhanced adaptive L1 penalty and weighted fusion penalty into the sparse learning model, we select the most informative features from the matrix. Specifically, we measure the importance of features using mutual information and introduce an adaptive weight construction strategy to penalize regression coefficients corresponding to each variable adaptively. Moreover, the weighted fusion penalty balances weight differences among correlated variables, reducing the model’s overreliance on specific variables and enhancing accuracy. The performance of the proposed method was validated on BCI Competition IV datasets IIa and IIb using the support vector machine. Experimental results demonstrate the effectiveness and superiority of the proposed model compared to the existing models.

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Fused lasso for feature selection using structural information

Cited by 33 publications

References 33 publications

Predicting Math Performance in High School Students using Machine Learning Techniques

Predicting Math Performance in High School Students using Machine Learning Techniques

GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification

Efficient Feature Learning Model of Motor Imagery EEG Signals with L1-Norm and Weighted Fusion

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