Genetic Programming with Multi-Task Feature Selection for Alzheimer's Disease Diagnosis
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{tang:2024:CEC,
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author = "Shanshan Tang and Qi Chen and Bing Xue and
Min Huang and Mengjie Zhang",
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title = "Genetic Programming with Multi-Task Feature Selection
for {Alzheimer's} Disease Diagnosis",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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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",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10611973",
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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.",
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notes = "also known as \cite{10611973}
WCCI 2024",
- }
Genetic Programming entries for
Shanshan Tang
Qi Chen
Bing Xue
Min Huang
Mengjie Zhang
Citations