FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2023448113> ?p ?o ?g. }

• W2023448113 endingPage "229" @default.
• W2023448113 startingPage "214" @default.
• W2023448113 abstract "Most methods for the evolutionary generation of multi-layer perceptron classifiers use a divide-and-conquer strategy, where the tasks of feature selection, structure design, and weight training are performed separately. The concurrent evolution of the whole classifier has been seldom attempted and its effectiveness has never been exhaustively benchmarked. This paper presents an experimental study on the merits of this latter approach. Two schemes were investigated. The first method evolves simultaneously the neural network structure and input feature vector, and trains via a standard learning procedure the candidate solutions (wrapper approach). The second method evolves simultaneously the whole classifier (embedded approach). The performance of these two algorithms was compared to that of two manual and two automatic neural network optimisation techniques on thirteen well-known pattern recognition benchmarks. The experimental results revealed the specific strengths and weaknesses of the six algorithms. Overall, the evolutionary embedded method obtained good results in terms of classification accuracy and compactness of the solutions. The tests indicated that the outcome of the feature selection task has a major impact on the accuracy and compactness of the solutions. Evolutionary algorithms perform best on feature spaces of small and medium size, and were the most effective at rejecting redundant features. Classical filter-based algorithms based on feature correlation are preferable on undersampled data sets. Correlation- and saliency-based selection was the most effective method in the presence of a large number of irrelevant features. The applicability and performance of the wrapper algorithm was severely limited by the computational costs of the approach." @default.
• W2023448113 created "2016-06-24" @default.
• W2023448113 creator A5079759495 @default.
• W2023448113 date "2013-01-01" @default.
• W2023448113 modified "2023-10-14" @default.
• W2023448113 title "Evolutionary generation of neural network classifiers—An empirical comparison" @default.
• W2023448113 cites W1500895378 @default.
• W2023448113 cites W1511704128 @default.
• W2023448113 cites W1564754565 @default.
• W2023448113 cites W1576581280 @default.
• W2023448113 cites W1578950828 @default.
• W2023448113 cites W1613756701 @default.
• W2023448113 cites W1763200058 @default.
• W2023448113 cites W1808644423 @default.
• W2023448113 cites W1813219539 @default.
• W2023448113 cites W1855251504 @default.
• W2023448113 cites W1879433572 @default.
• W2023448113 cites W1931353891 @default.
• W2023448113 cites W1981959369 @default.
• W2023448113 cites W1986477256 @default.
• W2023448113 cites W1992616163 @default.
• W2023448113 cites W2002007249 @default.
• W2023448113 cites W2009972770 @default.
• W2023448113 cites W2012027153 @default.
• W2023448113 cites W2017337590 @default.
• W2023448113 cites W202323902 @default.
• W2023448113 cites W2037592308 @default.
• W2023448113 cites W2040604977 @default.
• W2023448113 cites W2040884411 @default.
• W2023448113 cites W2046593516 @default.
• W2023448113 cites W2050870241 @default.
• W2023448113 cites W2052642131 @default.
• W2023448113 cites W2054281993 @default.
• W2023448113 cites W2059322780 @default.
• W2023448113 cites W2069928051 @default.
• W2023448113 cites W2073849130 @default.
• W2023448113 cites W2081547991 @default.
• W2023448113 cites W2082381659 @default.
• W2023448113 cites W2090701332 @default.
• W2023448113 cites W2100253618 @default.
• W2023448113 cites W2101675796 @default.
• W2023448113 cites W2103385178 @default.
• W2023448113 cites W2107721024 @default.
• W2023448113 cites W2110179049 @default.
• W2023448113 cites W2111072639 @default.
• W2023448113 cites W2112061072 @default.
• W2023448113 cites W2112320627 @default.
• W2023448113 cites W2124290836 @default.
• W2023448113 cites W2128630570 @default.
• W2023448113 cites W2129272928 @default.
• W2023448113 cites W2132678073 @default.
• W2023448113 cites W2134514463 @default.
• W2023448113 cites W2135458935 @default.
• W2023448113 cites W2137246835 @default.
• W2023448113 cites W2138784882 @default.
• W2023448113 cites W2138845388 @default.
• W2023448113 cites W2142308855 @default.
• W2023448113 cites W2145085734 @default.
• W2023448113 cites W2150381039 @default.
• W2023448113 cites W2152907412 @default.
• W2023448113 cites W2159771557 @default.
• W2023448113 cites W2165132362 @default.
• W2023448113 cites W2168810081 @default.
• W2023448113 cites W2193378034 @default.
• W2023448113 cites W2915695551 @default.
• W2023448113 cites W2015537396 @default.
• W2023448113 doi "https://doi.org/10.1016/j.neucom.2012.07.010" @default.
• W2023448113 hasPublicationYear "2013" @default.
• W2023448113 type Work @default.
• W2023448113 sameAs 2023448113 @default.
• W2023448113 citedByCount "23" @default.
• W2023448113 countsByYear W20234481132014 @default.
• W2023448113 countsByYear W20234481132015 @default.
• W2023448113 countsByYear W20234481132016 @default.
• W2023448113 countsByYear W20234481132017 @default.
• W2023448113 countsByYear W20234481132018 @default.
• W2023448113 countsByYear W20234481132020 @default.
• W2023448113 countsByYear W20234481132021 @default.
• W2023448113 countsByYear W20234481132022 @default.
• W2023448113 countsByYear W20234481132023 @default.
• W2023448113 crossrefType "journal-article" @default.
• W2023448113 hasAuthorship W2023448113A5079759495 @default.
• W2023448113 hasConcept C11413529 @default.
• W2023448113 hasConcept C119857082 @default.
• W2023448113 hasConcept C138885662 @default.
• W2023448113 hasConcept C148483581 @default.
• W2023448113 hasConcept C153180895 @default.
• W2023448113 hasConcept C154945302 @default.
• W2023448113 hasConcept C159149176 @default.
• W2023448113 hasConcept C179717631 @default.
• W2023448113 hasConcept C2776401178 @default.
• W2023448113 hasConcept C41008148 @default.
• W2023448113 hasConcept C41895202 @default.
• W2023448113 hasConcept C50644808 @default.
• W2023448113 hasConcept C60908668 @default.
• W2023448113 hasConcept C71559656 @default.
• W2023448113 hasConcept C95623464 @default.
• W2023448113 hasConceptScore W2023448113C11413529 @default.

• next