Statistical hypothesis testing for chemical detection in changing environments

This paper addresses the problem of adaptive chemical detection, using the Receptor Density Algorithm (RDA), an immune inspired anomaly detection algorithm. Our approach is to first detect when and if something has changed in the environment and then adapt the RDA to this change. Statistical hypothesis testing is used to determine whether there has been concept drift in consecutive time windows of the data. Five different statistical methods are tested on mass spectrometry data, enhanced with artificial events that signify a changing environment. The results show that, while no one method is universally best, statistical hypothesis testing performs reasonably well on the context of chemical sensing and it can differentiate between anomalies and concept drift.

[1]  Jonathan Timmis,et al.  The Receptor Density Algorithm , 2013, Theor. Comput. Sci..

[2]  Jayant Kumar,et al.  Sensitive and fast recognition of explosives using fluorescent polymer sensors and pattern recognition analysis , 2011 .

[3]  Ingrid Renz,et al.  Adaptive Information Filtering : Learning Drifting Concepts , 1998 .

[4]  P. E. Keller,et al.  Electronic noses and their applications , 1995, IEEE Technical Applications Conference and Workshops. Northcon/95. Conference Record.

[5]  Alexey Tsymbal,et al.  The problem of concept drift: definitions and related work , 2004 .

[6]  Jonathan Timmis,et al.  Chemical Detection Using the Receptor Density Algorithm , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[7]  A. Bifet,et al.  Early Drift Detection Method , 2005 .

[8]  Bernhard Schölkopf,et al.  A Kernel Method for the Two-Sample-Problem , 2006, NIPS.

[9]  João Gama,et al.  Learning with Drift Detection , 2004, SBIA.

[10]  Gopal Kanji,et al.  100 Statistical Tests , 1994 .

[11]  Manuele Bicego,et al.  A comparative analysis of basic pattern recognition techniques for the development of small size electronic nose , 2002 .

[12]  G. Eiceman,et al.  Classification of ion mobility spectra by functional groups using neural networks. , 1999, Analytica chimica acta.

[13]  N. Ancona,et al.  Support vector machines for olfactory signals recognition , 2003 .

[14]  Hendrik Richter,et al.  Detecting change in dynamic fitness landscapes , 2009, 2009 IEEE Congress on Evolutionary Computation.

[15]  Mykola Pechenizkiy,et al.  Handling Concept Drift in Process Mining , 2011, CAiSE.

[16]  Gerhard Widmer,et al.  Learning in the presence of concept drift and hidden contexts , 2004, Machine Learning.

[17]  Anton Dries,et al.  Adaptive concept drift detection , 2009, SDM.

[18]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[19]  J. Friedman,et al.  Multivariate generalizations of the Wald--Wolfowitz and Smirnov two-sample tests , 1979 .

[20]  Shai Ben-David,et al.  Detecting Change in Data Streams , 2004, VLDB.

[21]  Koichiro Yamauchi,et al.  Detecting Concept Drift Using Statistical Testing , 2007, Discovery Science.

[22]  Ricard Gavaldà,et al.  Learning from Time-Changing Data with Adaptive Windowing , 2007, SDM.

[23]  David Byer,et al.  Real‐time detection of intentional chemical contamination in the distribution system , 2005 .