Learning in the presence of sudden concept drift and measurement drift

The current availability of vast data storage and the computational power to enact algorithms for interpreting that data in real time leads to the possibility of real time adaptive systems. Because change is nearly always inevitable, companies must strive to increase the adaptability of their manufacturing or service systems. To accomplish this, the methods for correcting the system and determining the correct change point must be studied. The motivation of this thesis is advancing the ability of proper prediction and classification model learning on data streams containing change. This problem is known as concept drift. Motivation also stems from a study on a system with these properties, at an active manufacturing facility. After reviewing articles relating to the specific problem in the study, a similarity between the study and the studies performed in the research area of advanced process control became clear. The underlying cause for the change in the manufacturing system is identified as measurement drift. The identification of measurement drift is explained. A discussion of the mathematical model representing measurement drift is provided. Existing concept drift algorithms are adapted to fit the needs of the measurement drift problem. Their performance on the data from the study and synthetic data sets mimicking varying levels of drift magnitude and frequency is assessed. The results are compared to a popular advanced process control method, exponential weighted moving average adapting intercept (EWMA-I). The advanced process control literature inspired the development of two new methods for learning in the presence of concept drift. The methods, ADMEAN and CD-EWMA (ADaptive MEAN and Concept Drift Exponential Weighted Moving Average), make changes to the incoming stream of independent variables. The performance of these algorithms on the measurement drift datasets and synthetic concept drift datasets is provided

Monitoring Radiation Use in Cardiac Fluoroscopy Imaging Procedures

Objective: Timely identification of systematic changes in radiation delivery of an imaging system can lead to a reduction in risk for the patients involved. However, existing quality assurance programs involving the routine testing of equipment performance using phantoms are limited in their ability to effectively carry out this task. To address this issue we propose the implementation of an ongoing monitoring process that utilizes procedural data to identify unexpected large or small radiation exposures for individual patients, as well as to detect persistent changes in the radiation output of imaging platforms. Methods: Data used in this study were obtained from records routinely collected during procedures performed in the cardiac catheterization imaging facility at St Andrew\u27s War Memorial Hospital, Brisbane, Australia over the period January 2008 to March 2010. A two stage monitoring process employing individual and exponentially weighted moving average (EWMA) control charts was developed and used to identify unexpectedly high or low radiation exposure levels for individual patients, as well as detect persistent changes in the radiation output delivered by the imaging systems. To increase sensitivity of the charts we account for variation in dose area product (DAP) values due to other measured factors (patient weight, fluoroscopy time, digital acquisition frame count) using multiple linear regression. Control charts are then constructed using the residual values from this linear regression. The proposed monitoring process was evaluated using simulation to model performance of the process under known conditions. Results: Retrospective application of this technique to actual clinical data identified a number of cases in which the DAP result could be considered unexpected. Most of these, upon review, were attributed to data entry errors. The charts monitoring overall system radiation output trends demonstrated changes in equipment performance associated with relocation of the equipment to a new department. When tested under simulated conditions, the EWMA chart was capable of detecting a sustained 15% increase in average radiation output within 60 cases (\u3c 1 month of operation) while a 33% increase would be signalled within 20 cases. Conclusion: This technique offers a valuable enhancement to existing quality assurance programs in radiology that rely upon the testing of equipment radiation output at discrete time frames to ensure performance security

Review on Concept Drift Detection Techniques

Detecting the changes and reacting on them is an interesting research topic in current era. Concept drift detection is comes under data stream mining. Process which takeout information from data stream which continuously generated called data stream mining. Normally in data set the data is stationary but problem arises when data is continuously generated that is data stream. So in that case the detection of concept drift is an important task. There are various techniques for drift detection. This paper focuses on some main technique of drift detection

Detecting the process\u27 1.5 sigma shift: A quantitative study

Process behavior can change with time. In this study an attempt was made to discover whether the Six Sigma™ claim of changes in the process mean stayed within +/- 1.5 sigma units. Several process groups were examined for a particular firm that made metal castings, machined parts, tested major components and assembled these into a vehicle that was a product sold to the customer. As the assembly progressed, deficiencies were identified and recorded. Analyses employed cumulative sum (CUSUM) sequence charts, Autoregressive Integrated Moving Average (ARIMA) time series analyses, minimum mean square error (MMSE) exponentially weighted moving average (EWMA), Shewhart control charts and Analysis of Variance (ANOVA) to identify the shift in the process mean, M/sw, the duration of the shift, λB, and the proper choice of EWMA smoothing coefficient, λEWMA. Kruskal-Wallis analysis of the relationship of these measures to process group (assembly, foundry, heat treatment, machining, shaving, test machine, grinding, turning, warranty and yield) was also performed. The method used was generally applicable for all these processes. The process group and the ARIMA type also influenced the measurement of M/sw , λB , and λEWMA