Possibilistic nonlinear dynamical analysis for pattern recognition

被引:2
|
作者
Pham, Tuan D. [1 ]
机构
[1] Univ Aizu, Ctr Adv Informat Sci & Technol, Aizu Wakamatsu, Fukushima 9658580, Japan
基金
澳大利亚研究理事会; 日本学术振兴会;
关键词
Nonlinear dynamics; Entropy measures; Possibility; Fuzzy sets; Geostatistics; Biosignals; Pattern recognition; MASS-SPECTROMETRY DATA; TIME-SERIES ANALYSIS; APPROXIMATE ENTROPY; BIOMARKER DISCOVERY; PERSONALIZED MEDICINE; SERUM BIOMARKERS; SAMPLE ENTROPY; OVARIAN-CANCER; BREAST-CANCER; FUZZY-SETS;
D O I
10.1016/j.patcog.2012.09.025
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A nonlinear dynamical system can be defined as a study of any system that implies motion, change, or evolution in time where a change in one variable is not proportional to a change in a related variable. The mathematical operations underlying such a system are very useful for pattern recognition with time-series data. One of the most recent developments in nonlinear dynamical analysis is the so-called approximate entropy family. However, its algorithms are deterministic and do not consider uncertainty where the modeling of possibility can be appropriate and advantageous in many practical situations. Thus, possibilistic entropy algorithms are proposed in this paper as a new methodology for nonlinear dynamical analysis. The proposed approach is based on the notions of the approximate entropy family, geostatistics, and the theory of fuzzy sets. Furthermore, for the first time, nonlinear dynamical analysis of mass spectrometry data is presented for computer-based recognition of potential protein biomarkers and classification, which can be utilized for early disease prediction. Experimental results using proteomic and genetic data have shown the potential application of the proposed possibilistic nonlinear dynamical analysis to the study of complex biosignals. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:808 / 816
页数:9
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