Capability of Classification of Control Chart Patterns Classifiers Using Symbolic Representation Preprocessing and Evolutionary Computation

Abstract

Ability to monitor and detect abnormalities accurately is important in a manufacturing process. This can be achieved by recognizing abnormalities in its control charts. This work is concerned with classification of control chart patterns (CCPs) by utilizing a technique known as Symbolic Aggregate Approximation (SAX) and an evolutionary based data mining program known as Self-adjusting Association Rules Generator (SARG). SAX is used in preprocessing to transform CCPs, which can be considered as time series, to symbolic representations. SARG is then applied to these symbolic representations to generate a classifier in a form of a nested IF-THEN-ELSE rules. A more efficient nested IF-THEN-ELSE rules classifier in SARG is discovered. A systematic investigation was carried out to find the capability of the proposed method. This was done by attempting to generate classifiers for CCPs datasets with different level of noises in them. CCPs were generated by Generalized Autoregressive Conditional Heteroskedasticity (GARH) Model where ó is the noise level parameter. Two crucial parameters in SAX are Piecewise Aggregate Approximation and Alphabet Size values. This work identifies suitable values for both parameters in SAX for SARG to generate CCPs classifiers. This is the first work to generate CCPs classifiers with accuracy up to 90% for ó at 13 and 95 % for ó at 9.