A classification system based on a new wrapper feature selection algorithm for the diagnosis of primary and secondary polycythemia

dc.contributor.author	Κορφιάτης, Βασίλειος Χρ.	el
dc.contributor.author	Ασβεστάς, Παντελής Α.	el
dc.contributor.author	Ντελιμπάσης, Κωνσταντίνος Κ.	el
dc.contributor.author	Ματσόπουλος, Γεώργιος Κ.	el
dc.date.accessioned	2015-05-17T21:44:23Z
dc.date.available	2015-05-17T21:44:23Z
dc.date.issued	2015-05-18
dc.identifier.uri	http://hdl.handle.net/11400/10635
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.sciencedirect.com/science/article/pii/S0010482513002679#	el
dc.subject	Classification systems
dc.subject	Machine learning
dc.subject	Polycythemia
dc.subject	LM–FM wrapper
dc.subject	LM-FM περιτύλιγμα
dc.subject	Συστήματα ταξινόμησης
dc.subject	Μηχανή μάθησης
dc.subject	Multiclass SVM
dc.subject	Maximum output information
dc.subject	Η μέγιστη πληροφορία εξόδου
dc.subject	Πολυκυτταραιμία
dc.title	A classification system based on a new wrapper feature selection algorithm for the diagnosis of primary and secondary polycythemia	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.keywordURI	http://skos.um.es/unescothes/C00619
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85079324
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85104610
heal.identifier.secondary	doi:10.1016/j.compbiomed.2013.09.016
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2013-12
heal.bibliographicCitation	Korfiatis, V. C., Asvestas, P. A., Delibasis, K. K. and Matsopoulos, G. K. (2013). A classification system based on a new wrapper feature selection algorithm for the diagnosis of primary and secondary polycythemia. "Computers in Biology and Medicine", 43(12), December 2013. pp. 2118-2126. Available from: http://www.sciencedirect.com/science/article/pii/S0010482513002679#. [Accessed 28/09/2013]	en
heal.abstract	Primary and Secondary Polycythemia are diseases of the bone marrow that affect the blood's composition and prohibit patients from becoming blood donors. Since these diseases may become fatal, their early diagnosis is important. In this paper, a classification system for the diagnosis of Primary and Secondary Polycythemia is proposed. The proposed system classifies input data into three classes; Healthy, Primary Polycythemic (PP) and Secondary Polycythemic (SP) and is implemented using two separate binary classification levels. The first level performs the Healthy/non-Healthy classification and the second level the PP/SP classification. To this end, a novel wrapper feature selection algorithm, called the LM–FM algorithm, is presented in order to maximize the classifier's performance. The algorithm is comprised of two stages that are applied sequentially: the Local Maximization (LM) stage and the Floating Maximization (FM) stage. The LM stage finds the best possible subset of a fixed predefined size, which is then used as an input for the next stage. The FM stage uses a floating size technique to search for an even better solution by varying the initially provided subset size. Then, the Support Vector Machine (SVM) classifier is used for the discrimination of the data at each classification level. The proposed classification system is compared with various well-established feature selection techniques such as the Sequential Floating Forward Selection (SFFS) and the Maximum Output Information (MOI) wrapper schemes, and with standalone classification techniques such as the Multilayer Perceptron (MLP) and SVM classifier. The proposed LM–FM feature selection algorithm combined with the SVM classifier increases the overall performance of the classification system, scoring up to 98.9% overall accuracy at the first classification level and up to 96.6% at the second classification level. Moreover, it provides excellent robustness regardless of the size of the input feature subset used.	en
heal.journalName	Computers in Biology and Medicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true