Paper Info
Title
Optimization of symbolic feature extraction for pattern classification
Abstract
The concept of symbolic dynamics has been used in recent literature for feature extraction from time series data for pattern classification. The two primary steps of this technique are partitioning of time series to optimally generate symbol sequences and subsequently modeling of state machines from such symbol sequences. The latter step has been widely investigated and reported in the literature. However, for optimal feature extraction, the first step needs to be further explored. The paper addresses this issue and proposes a data partitioning procedure to extract low-dimensional features from time series while optimizing the class separability. The proposed procedure has been validated on two examples: (i) parameter identification in a Duffing system and (ii) classification of fatigue damage in mechanical structures, made of polycrystalline alloys. In each case, the classification performance of the proposed data partitioning method is compared with those of two other classical data partitioning methods, namely uniform partitioning (UP) and maximum entropy partitioning (MEP).
Year
DOI
Venue
2012
10.1016/j.sigpro.2011.08.013
Signal Processing
Keywords
Field
DocType
symbolic feature extraction,time series,maximum entropy partitioning,feature extraction,uniform partitioning,symbol sequence,pattern classification,time series data,proposed data,classical data,classification performance,time series analysis
Symbolic dynamics,Time series,Pattern recognition,Computer science,Algorithm,Feature extraction,Finite-state machine,Artificial intelligence,Principle of maximum entropy,Class separability,Data partitioning
Journal
Volume
Issue
ISSN
92
3
0165-1684
Citations 
PageRank 
References 
6
0.52
9
Authors
5
Name
Order
Citations
PageRank
Soumik Sarkar119728.75
Kushal Mukherjee210512.83
Xin Jin3555.74
Dheeraj S. Singh460.86
Ray, A.5832184.32