کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
563428 875494 2012 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Optimization of symbolic feature extraction for pattern classification
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
Optimization of symbolic feature extraction for pattern classification
چکیده انگلیسی

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).


► Development of a novel time-series partitioning tool for feature extraction.
► Optimization of class separability for pattern classification.
► Trade-off among robustness to disturbances, sensitivity to data characteristics, and quantization error.
► Experimental validation in anomaly detection and damage classification.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 92, Issue 3, March 2012, Pages 625–635
نویسندگان
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