Recognition efficiency enhancement of control chart pattern using ensemble MLP neural network

Bibliographic Data

ID15795387
AuthorsSapna Kadakadiyavar (corresponding author), Achyutha Prasad N (0000-0002-2356-4146), Achyutha N Prasad, Piyush Kumar Pareek (0000-0003-2287-0122), Vani Vasudevan (Meenakshi Academy of Higher Education and Research), V Vani, V S Rekha (Dr. Hari Singh Gour University), G Nirmala (Sri Siddhartha Medical College)
Year2022
Pages4295-4306
Publication date2022-05-01
Open AccessYes
TypeARTICLE
VenueInternational Journal of Health Sciences (JOURNAL)
Journal identifiersISSN: 2550-696X • E-ISSN: 2550-6978
PublisherUniversidad Tecnica de Manabi (PUBLISHER • EC)
DOI10.53730/ijhs.v6ns3.6851
OpenAlexW4225263206
LanguageEN
Citations received4
References cited2

chart pattern for six different fundamental patterns have been proposed. The multilayer perceptron based neural network has applied as an entity to form the ensemble. The loss of generalization property of an individual neural network has been fulfilled by providing the diversity in the training data for the individual entity of ensemble. The outcome of individual recognizer has combined over their decision value to include the outcome knowledge of each and every entity instead of majority voting of decision. The proposed method has been verified over a large set of data experimentally and observed that there is significant improvement with proposed ensemble approach in compare to individual recognizer

Artificial neural network · Chart · Control chart · Data mining · Ensemble learning · Generalization · Machine learning · Majority rule · Multilayer perceptron · Outcome (game theory · Pattern recognition (psychology · Perceptron · Process (computing · Property (philosophy · Set (abstract data type · Statistics · Voting · Advanced Statistical Process Monitoring · Computer Science · Fault Detection and Control Systems · Mathematics · Artificial Intelligence

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