Tapped Delay Lines for GP Streaming Data Classification with Label Budgets

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Vahdat:2015:EuroGP,

• author = "Ali Vahdat and Jillian Morgan and Andrew R. McIntyre and Malcolm I. Heywood and A. Nur Zincir-Heywood",
• title = "Tapped Delay Lines for {GP} Streaming Data Classification with Label Budgets",
• booktitle = "18th European Conference on Genetic Programming",
• year = "2015",
• editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim",
• series = "LNCS",
• volume = "9025",
• publisher = "Springer",
• pages = "126--138",
• address = "Copenhagen",
• month = "8-10 " # apr,
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming, Streaming data classification, Non-stationary, Class imbalance, Benchmarking",
• isbn13 = "978-3-319-16500-4",
• DOI = "doi:10.1007/978-3-319-16501-1_11",
• abstract = "Streaming data classification requires that a model be available for classifying stream content while simultaneously detecting and reacting to changes to the underlying process generating the data. Given that only a fraction of the stream is visible at any point in time (i.e. some form of window interface) then it is difficult to place any guarantee on a classifier encountering a well mixed distribution of classes across the stream. Moreover, streaming data classifiers are also required to operate under a limited label budget (labelling all the data is too expensive). We take these requirements to motivate the use of an active learning strategy for decoupling genetic programming training epochs from stream throughput. The content of a data subset is controlled by a combination of Pareto archiving and stochastic sampling. In addition, a significant benefit is attributed to support for a tapped delay line (TDL) interface to the stream, but this also increases the dimensionality of the task. We demonstrate that the benefits of assuming the TDL can be maintained through the use of oversampling without recourse to additional label information. Benchmarking on 4 dataset demonstrates that the approach is particularly effective when reacting to shifts in the underlying properties of the stream. Moreover, an online formulation for class-wise detection rate is assumed, where this is able to robustly characterise classifier performance throughout the stream.",
• notes = "Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015",

}

Genetic Programming entries for Ali Vahdat Jillian Morgan Andrew R McIntyre Malcolm Heywood Nur Zincir-Heywood

Citations