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We investigate the potential of neural-network based classifiers for discriminating gravitational wave bursts (GWBs) of a given canonical family (e.g. core-collapse supernova waveforms) from typical transient instrumental artifacts (glitches), in the data of a single detector. The further classification of glitches into typical sets is explored. In order to provide a proof of concept, we use the core-collapse supernova waveform catalog produced by H. Dimmelmeier and co-Workers, and the data base of glitches observed in laser interferometer gravitational wave observatory (LIGO) data maintained by P. Saulson and co-Workers to construct datasets of (windowed) transient waveforms (glitches and bursts) in additive (Gaussian and compound-Gaussian) noise with different signal-to-noise ratios (SNR). Principal component analysis (PCA) is next implemented for reducing data dimensionality, yielding results consistent with, and extending those in the literature. Then, a multilayer perceptron is trained by a backpropagation algorithm (MLP-BP) on a data subset, and used to classify the transients as glitch or burst. A Self-Organizing Map (SOM) architecture is finally used to classify the glitches. The glitch/burst discrimination and glitch classification abilities are gauged in terms of the related truth tables. Preliminary results suggest that the approach is effective and robust throughout the SNR range of practical interest. Perspective applications pertain both to distributed (network, multisensor) detection of GWBs, where some intelligence at the single node level can be introduced, and instrument diagnostics/optimization, where spurious transients can be identified, classified and hopefully traced back to their entry points.

Most traditional postcode recognition systems implicitly assumed that the distribution of the 10 numerals (0–9) is balanced. However it is far from a reasonable setting because the distribution of 0–9 in postcodes of a country or a city is generally imbalanced. Some numerals appear in more postcodes, while some others do not. In this paper, we study cost-sensitive neural network classifiers to address the class imbalance problem in postcode recognition. Four methods, namely: cost-sampling, cost-convergence, rate-adapting and threshold-moving are considered in training neural networks. Cost-sampling adjusts the distribution of the training data such that the costs of classes are conveyed explicitly by the appearances of their instances. Cost-convergence and rate-adapting are carried out in training phase by modifying the architecture of training algorithms of the neural network. Threshold-moving tries to increase the probability estimations of expensive classes to avoid the samples with higher costs to be misclassified. 10,702 postcode images are experimented using five cost matrices based on the distribution of numerals in postcodes. The results suggest that cost-sensitive learning is indeed effective on class imbalanced postcode analysis and recognition. It also reveals that cost-sampling on a proper cost matrix outperforms others in this application.