Genetic Programming with Multi-Task Feature Selection for Alzheimer's Disease Diagnosis

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{tang:2024:CEC,

• author = "Shanshan Tang and Qi Chen and Bing Xue and Min Huang and Mengjie Zhang",
• title = "Genetic Programming with Multi-Task Feature Selection for {Alzheimer's} Disease Diagnosis",
• booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2024",
• editor = "Bing Xue",
• address = "Yokohama, Japan",
• month = "30 " # jun # " - 5 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Accuracy, Parkinson's disease, Biomarkers, Multitasking, Feature extraction, Prediction algorithms, Space exploration, Symbolic regression, Alzheimer's disease prediction, Feature selection",
• isbn13 = "979-8-3503-0837-2",
• DOI = "doi:10.1109/CEC60901.2024.10611973",
• abstract = "Alzheimer's disease (AD) has been the most common cause of dementia making cognitive score prediction and important feature identification crucial for its diagnosis. Although sparse linear regression has been used for this purpose due to its simplicity, it often selects an excessive number of features to track the disease and assumes a linear relationship between input and output, which might not always hold. To address these limitations, genetic programming-based symbolic regression (GPSR) algorithms have been proposed. GPSR can select the important features by exploring the feature space and learning a regression model without any assumption of model structure. However, the generalisation ability of existing GPSR methods still needs to be improved. Considering the multiple related prediction tasks in AD studies, this work proposes a new method called linear scaled GPSR with multi-task feature selection (LSGPMTFS), to promote the prediction performance of each task by knowledge-sharing among multiple tasks. LSGPMTFS has two stages. The first stage learns a specific feature subset for each task. In the second stage, the model for each task is searched on the union of feature subsets selected from the first stage. The experimental results on authentic AD datasets demonstrate that the proposed algorithm can select a small set of important features with better learning and generalisation performance compared with other GPSR methods.",
• notes = "also known as \cite{10611973}

  WCCI 2024",

}

Genetic Programming entries for Shanshan Tang Qi Chen Bing Xue Min Huang Mengjie Zhang

Citations