SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming
نویسندگان
چکیده
We present SLUG, a method that uses genetic algorithms as wrapper for programming (GP), to perform feature selection while inducing models. This is first tested on four regular binary classification datasets, and then 10 synthetic datasets produced by GAMETES, tool embedding epistatic gene-gene interactions into noisy datasets. compare the results of SLUG with ones obtained other GP-based methods had already been used GAMETES problems, concluding proposed approach very successful, particularly discuss merits weaknesses its various parts, i.e. learner, we additional experiments, aimed at comparing state-of-the-art learners, like decision trees, random forests extreme gradient boosting. Despite fact not most efficient in terms training time, it confirmed effective accuracy.
منابع مشابه
automatic verification of authentication protocols using genetic programming
implicit and unobserved errors and vulnerabilities issues usually arise in cryptographic protocols and especially in authentication protocols. this may enable an attacker to make serious damages to the desired system, such as having the access to or changing secret documents, interfering in bank transactions, having access to users’ accounts, or may be having the control all over the syste...
15 صفحه اولFeature selection and molecular classification of cancer using genetic programming.
Despite important advances in microarray-based molecular classification of tumors, its application in clinical settings remains formidable. This is in part due to the limitation of current analysis programs in discovering robust biomarkers and developing classifiers with a practical set of genes. Genetic programming (GP) is a type of machine learning technique that uses evolutionary algorithm t...
متن کاملGenetic Algorithms for Feature Selection and Weighting
Automated techniques to optimise the retrieval of relevant cases in a CBR system are desirable as a way to reduce the expensive knowledge acquisition phase. This paper concentrates on feature selection methods that assist in indexing the case-base, and feature weighting methods that improve the similarity-based selection of relevant cases. Two main types of method are presented: filter methods ...
متن کاملMessy Genetic Algorithms for Subset Feature Selection
Subset Feature Selection problems can have several attributes which may make Messy Ge netic Algorithms an appropriate optimization method First competitive solutions may of ten use only a small percentage of the total available features this can not only o er an advantage to Messy Genetic Algorithms it may also cause problems for other types of evolutionary algorithms Second the evalu ation of ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2022
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-031-02056-8_5