Article
Computer Science, Information Systems
Mehrdad Vatankhah, Mohammadreza Momenzadeh
Summary: In this article, a new method is introduced to improve the performance of the Lasso feature selection model. The method finds the best regularization parameter automatically to achieve optimal performance in DNA microarray data classification. Experimental results demonstrate that the proposed Lasso outperforms other feature selection methods in terms of selecting the best features for microarray data classification, showing robustness and stability. It is a powerful algorithm for selecting informative features, which can be applied in cancer diagnosis using gene expression profiles.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Sadegh Salesi, Georgina Cosma
Summary: Evolutionary Computation (EC) algorithms are powerful techniques for feature selection, but they often suffer from the stability issue of reaching different solutions in each run. This paper introduces a novel algorithm called Generalisation Power Analysis (GPA) to evaluate feature subsets based on their generalisation power over multiple classifiers, outperforming alternative methods in achieving high generalisation power. Despite requiring more computation time, using GPA during feature selection results in a robust prediction model developed with features not biased towards a specific classifier.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Biochemical Research Methods
Wenjian Xu, Haochen He, Zhengguang Guo, Wei Li
Summary: This study evaluated machine learning models for predicting protein expression levels using RNA expression profiles. The results showed that appropriate feature selection methods combined with classical machine learning models could achieve excellent predictive performance. Applying the model to brain transcriptome data helped infer protein profiles for better understanding of brain region functions.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemistry & Molecular Biology
Leqi Tian, Wenbin Wu, Tianwei Yu
Summary: Random Forest (RF) is a popular machine learning method for classification and regression tasks, and it performs well under low sample size situations. However, there are issues with gene selection using RF as the important genes are usually scattered on the gene network, which conflicts with the biological assumption of functional consistency. To address this issue, we propose the Graph Random Forest (GRF) method, which incorporates external topological information to identify highly connected important features. The algorithm achieves equivalent classification accuracy to RF while selecting interpretable feature sub-graphs.
Article
Computer Science, Artificial Intelligence
Weichan Zhong, Xiaojun Chen, Qingyao Wu, Min Yang, Joshua Zhexue Huang
Summary: This paper proposes a diverse feature selection method (DFS) which performs feature clustering and selection simultaneously, and introduces diverse regularization (DR) to reduce correlation among important features. Experimental results demonstrate the superior performance of DFS.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Information Systems
Kaveh Kadkhoda Mohammadmosaferi, Hassan Naderi
Summary: The study introduces the AFIF method, which efficiently extracts structural features from social networks and predicts future changes in community evolution.
COMPUTER COMMUNICATIONS
(2021)
Article
Mathematics
Claudia Angelini, Daniela De Canditiis, Anna Plaksienko
Summary: This paper addresses the problem of estimating graphical models of conditional dependencies between variables from multiple datasets under Gaussian settings. The proposed jewel 2.0 method improves upon the previous version by modeling commonality and class-specific differences in the graph structures and incorporating a stability selection procedure to reduce false positives. The performance of jewel 2.0 is demonstrated through simulated and real data examples, and the method is implemented in the R package jewel.
Article
Mathematics
Feng Hong, Lu Tian, Viswanath Devanarayan
Summary: High-dimensional data applications require the use of statistical and machine-learning algorithms to identify optimal biomarker signatures based on patient characteristics for predicting clinical outcomes in biomedical research. Regularization, particularly L-1-based regularization, is commonly used to improve prediction performance and feature selection. However, choosing the penalty parameter for regularization can be unstable and may lead to inflated predictive performance estimates. This paper proposes a Monte Carlo approach for robust regularization parameter selection and an additional cross-validation wrapper for objectively evaluating the final model's predictive performance.
Article
Automation & Control Systems
Chin Gi Soh, Ying Zhu, Tin Lam Toh
Summary: The geographical origin of olive oil is important for consumers, as oils from regions known for olive cultivation are assumed to be of higher quality. However, determining the origin is challenging due to the similarity in chemical compositions of the oils. Fourier-transform infrared spectroscopy is a viable technology for classifying oil samples by origin, but traditional methods yield less interpretable models.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2023)
Article
Genetics & Heredity
Hongyu Wang, Zhaomin Yao, Renli Luo, Jiahao Liu, Zhiguo Wang, Guoxu Zhang
Summary: OMIC is a novel approach that analyzes genetic or molecular profiles and explores the correlation between different features using a convolutional neural network (CNN). By transforming transcriptomic features into LaCOme features, it has been shown to outperform the original transcriptomic features in classification performance. This method enhances computational analysis results and provides valuable information in OMIC data analysis.
Article
Computer Science, Artificial Intelligence
Wei Gao, Haizhong Yang
Summary: This study proposes a causality-based feature selection method by introducing time-varying Granger causal networks to capture the causal relationships in high-dimensional dynamic systems. It overcomes the limitations of sample scarcity and transforms the problem of learning Granger causal structures into a group variable selection problem. Experimental results demonstrate that the method is efficient in detecting changes and analyzing causal dependency structures in network evolution.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Sahar A. El-Rahman, Ala Saleh Alluhaidan, Reem A. AlRashed, Duna N. AlZunaytan
Summary: This paper presents a mobile application that analyzes patients' medical records and uses machine learning techniques to diagnose chronic conditions. The study shows that the tree algorithm achieves 100% accuracy for hypertension diagnosis, with the highest precision for both male and female datasets.
Article
Engineering, Multidisciplinary
Ke Wang, Ying An, Jiancun Zhou, Yuehong Long, Xianlai Chen
Summary: This study proposed a multi-level feature selection algorithm based on Lasso coefficient threshold (Coe-Thr-Lasso), which can effectively remove redundant and irrelevant features in radiomics, thus improving the quality of feature subset and the generalization ability of the model.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Computer Science, Hardware & Architecture
Ramraj Dangi, Praveen Lalwani
Summary: This article provides an overview of the hierarchy and architecture of 5G network slicing, and proposes the application of a machine learning network slicing model based on feature selection. The experimental results demonstrate that the proposed model performs exceptionally well in accurately predicting 5G network slices.
Article
Computer Science, Information Systems
Cun Ji, Mingsen Du, Yanxuan Wei, Yupeng Hu, Shijun Liu, Li Pan, Xiangwei Zheng
Summary: Time series classification is widely used in various domains, including EEG/ECG classification, device anomaly detection, and speaker authentication. Despite the existence of many methods, selecting intuitive temporal features for accurate classification remains a challenge. Therefore, this paper proposes a new method called TSC-RTF, which utilizes random temporal features, and shows that it can compete with state-of-the-art methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Ophthalmology
Lamis Baydoun, Lisanne Ham, Vincent Borderie, Isabel Dapena, Jingzhen Hou, Laurence E. Frank, Silke Oellerich, Gerrit R. J. Melles
JAMA OPHTHALMOLOGY
(2015)
Article
Ophthalmology
Korine van Dijk, Marina Rodriguez-Calvo-de-Mora, Hilde van Esch, Laurence Frank, Isabel Dapena, Lamis Baydoun, Silke Oellerich, Gerrit R. J. Melles
Article
Ophthalmology
Marina Rodriguez-Calvo de Mora, Esther A. Groeneveld-van Beek, Laurence E. Frank, Jacqueline van der Wees, Silke Oellerich, Marieke Bruinsma, Gerrit R. J. Melles
JAMA OPHTHALMOLOGY
(2016)
Article
Mathematics, Interdisciplinary Applications
Shahab Jolani, Laurence E. Frank, Stef van Buuren
BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY
(2014)
Article
Biochemical Research Methods
Janine H. Stubbe, Astrid M. J. Chorus, Laurence E. Frank, Olivier de Hon, Peter G. M. van der Heijden
DRUG TESTING AND ANALYSIS
(2014)
Article
Ophthalmology
Korine van Dijk, Jack Parker, Vasilios S. Liarakos, Lisanne Ham, Laurence E. Frank, Gerrit R. J. Melles
JOURNAL OF CATARACT AND REFRACTIVE SURGERY
(2013)
Article
Statistics & Probability
Gerko Vink, Laurence E. Frank, Jeroen Pannekoek, Stef van Buuren
STATISTICA NEERLANDICA
(2014)
Article
Ophthalmology
Silke Oellerich, Lisanne Ham, Laurence E. Frank, Sandra Gorges, Vincent J. A. Bourgonje, Lamis Baydoun, Korine Van Dijk, Gerrit R. J. Melles
AMERICAN JOURNAL OF OPHTHALMOLOGY
(2020)
Article
Environmental Sciences
Jeroen Knipscheer, Marieke Sleijpen, Laurence Frank, Ron de Graaf, Rolf Kleber, Margreet ten Have, Michel Dueckers
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2020)
Article
Ophthalmology
Janneau L. J. Claessens, Daniel A. Godefrooij, Gerko Vink, Laurence E. Frank, Robert P. L. Wisse
Summary: The study found novel associations between keratoconus and Hashimoto's thyroiditis as well as inflammatory skin conditions. It also confirmed known associations with atopic conditions such as allergic rash, asthma, bronchial hyperresponsiveness, and allergic rhinitis. The positive association between keratoconus and multiple immune-mediated diseases suggests that systemic inflammatory responses may play a role in its onset.
BRITISH JOURNAL OF OPHTHALMOLOGY
(2022)
Article
Multidisciplinary Sciences
M. B. Muijzer, C. M. W. Hoven, L. E. Frank, G. Vink, R. P. L. Wisse
Summary: Machine learning was used to explore the complex patterns of graft detachment after endothelial corneal transplantation surgery and evaluate the effects of practice pattern modulations. The results can assist surgeons in reviewing their practice patterns and generate hypotheses for empirical research on the origins of graft detachments.
SCIENTIFIC REPORTS
(2022)
Article
Ophthalmology
Silke Oellerich, Lamis Baydoun, Jorge Peraza-Nieves, Abbas Ilyas, Laurence Frank, Perry S. Binder, Gerrit R. J. Melles
Article
Ophthalmology
Nadine Gerber-Hollbach, Lamis Baydoun, Ester Fernandez Lopez, Laurence E. Frank, Isabel Dapena, Vasilios S. Liarakos, Sontje-Chiao Schaal, Lisanne Ham, Silke Oellerich, Gerrit R. J. Melles
Article
Ophthalmology
Jorge Peraza-Nieves, Lamis Baydoun, Isabel Dapena, Abbas Ilyas, Laurence E. Frank, Salvatore Luceri, Lisanne Ham, Silke Oellerich, Gerrit R. J. Melles
Article
Psychology, Clinical
Annemiek van Dijke, Julian D. Ford, Laurence E. Frank, Onno Van der Hart
JOURNAL OF TRAUMA & DISSOCIATION
(2015)