Distance based classifiers and their use to analysis of data concerned acute coronary syndromes

• Autorzy: Jóźwik A. , Stawska Z. , Grabowski M. , Filipiak K. , Rudowski R. , Opolski G.
• Języki publikacji: EN

Abstrakty

EN

The paper presents effectiveness of classifiers based on distance function in application to real problem concerned acute coronary syndromes. The types of decision rules: the standard k-NN rule; its fuzzy version and the multistage decision rule that uses the class overlap idea are considered. In the case of the fuzzy k-NN rule the fuzzyness is applied only for decreasing a misclassification rate. The multistage classifier is taken into account because of its very desired property, which consist in possibility of determination whether a case being classified is difficult or easy for recognition. The more difficult is the case to be classified the more stages are required. This property enables an error rate gradation. In each stage the proposed classifier can make up one of the three following decisions: indicate a class number, reply :"I do not know" or qualifythe object to the next stage. A number of stages depend on the classified object. The analyzed data concern to the two-class decision problem that consist in prediction whether the patient will survive the period of one month or not.

Słowa kluczowe

EN

cardiology; acute coronary syndromes; pattern recognition methods; multistage classifier; decision making; k-NN rule; fuzzy k-NN rule

PL

kardiologia; metoda rozpoznawania obrazów; klasyfikator wielostopniowy; podejmowanie decyzji

• Wydawca: Instytut Telekomunikacji i Informatyki Uniwersytetu Technologiczno-Przyrodniczego w Bydgoszczy
• Czasopismo: Image Processing & Communications
• Rocznik: 2003
• Tom: Vol. 9, no 3-4
• Strony: 45--52
• Opis fizyczny: Bibliogr. 5 poz., il.

Twórcy

autor

Jóźwik A.

• Institute Biocybernetics and Biomedical Engineering PAS, Trojdena 4, 02-109 Warsaw

autor

Stawska Z.

• Institute Biocybernetics and Biomedical Engineering PAS, Trojdena 4, 02-109 Warsaw

autor

Grabowski M.

• Department of Medical Informatics, The Medical University of Warsaw, Banacha 1a, 02-097 Warsaw
• Department of Internal Diseases and Cardiology, The Medical University of Warsaw, Banacha 1a, 02-097 Warsaw

autor

Filipiak K.

• Department of Internal Diseases and Cardiology, The Medical University of Warsaw, Banacha 1a, 02-097 Warsaw

autor

Rudowski R.

• Department of Medical Informatics, The Medical University of Warsaw, Banacha 1a, 02-097 Warsaw

autor

Opolski G.

• Department of Internal Diseases and Cardiology, The Medical University of Warsaw, Banacha 1a, 02-097 Warsaw

• Institute Biocybernetics and Biomedical Engineering PAS, Trojdena 4, 02-109 Warsaw

Bibliografia

• [1] E. Fix, J. L. Hodges, Discriminatory Analysis: Nonparametric Discrimination Small Sample Performance, project 21-49-004, Report Number 11, USAF School of Aviation Medicine, Randolph Field, Texas, pp. 280-322, 1952
• [2] A. Jóźwik, A learning scheme for a fuzzy k-NN rule, Patiern Recognition Letters, 1, pp.287 -289, 1983.
• [3] J. C. Bezdek, S. K. Chuah, D. Leep, Generalized k-NN rule, Fuzzy Sets and Systems, 18 , pp. 237-256, 1986.
• [4] A. Jóźwik, Z. Stawska, Wielostopniowy klasyfikator typu najbliższy sąsiad z każdej klasy, Materiały VIII Konferencji Sieci i Systemy Informatyczne, pp. 339-346, Łódź, 2000
• [5] Z. Stawska, A. Jóźwik , B. Sokołowska, K. Budzińska, A multistage classifier based on distance measure and its use for detection of respiration pathology, Komputerowe Systemy Rozpoznawania (referaty II Konferencji KOSYR2001), pp. 67-71, Wrocław, 2001