Network Synthesis

Contains reports on three research projects

Visual and auditory temporal integration in healthy younger and older adults

As people age, they tend to integrate successive visual stimuli over longer intervals than younger adults. It may be expected that temporal integration is affected similarly in other modalities, possibly due to general, age-related cognitive slowing of the brain. However, the previous literature does not provide convincing evidence that this is the case in audition. One hypothesis is that the primacy of time in audition attenuates the degree to which temporal integration in that modality extends over time as a function of age. We sought to settle this issue by comparing visual and auditory temporal integration in younger and older adults directly, achieved by minimizing task differences between modalities. Participants were presented with a visual or an auditory rapid serial presentation task, at 40-100 ms/item. In both tasks, two subsequent targets were to be identified. Critically, these could be perceptually integrated and reported by the participants as such, providing a direct measure of temporal integration. In both tasks, older participants integrated more than younger adults, especially when stimuli were presented across longer time intervals. This difference was more pronounced in vision and only marginally significant in audition. We conclude that temporal integration increases with age in both modalities, but that this change might be slightly less pronounced in audition

Identifying network communities with a high resolution

Community structure is an important property of complex networks. An automatic discovery of such structure is a fundamental task in many disciplines, including sociology, biology, engineering, and computer science. Recently, several community discovery algorithms have been proposed based on the optimization of a quantity called modularity (Q). However, the problem of modularity optimization is NP-hard, and the existing approaches often suffer from prohibitively long running time or poor quality. Furthermore, it has been recently pointed out that algorithms based on optimizing Q will have a resolution limit, i.e., communities below a certain scale may not be detected. In this research, we first propose an efficient heuristic algorithm, Qcut, which combines spectral graph partitioning and local search to optimize Q. Using both synthetic and real networks, we show that Qcut can find higher modularities and is more scalable than the existing algorithms. Furthermore, using Qcut as an essential component, we propose a recursive algorithm, HQcut, to solve the resolution limit problem. We show that HQcut can successfully detect communities at a much finer scale and with a higher accuracy than the existing algorithms. Finally, we apply Qcut and HQcut to study a protein-protein interaction network, and show that the combination of the two algorithms can reveal interesting biological results that may be otherwise undetectable.Comment: 14 pages, 5 figures. 1 supplemental file at http://cic.cs.wustl.edu/qcut/supplemental.pd

Prevention of childhood poisoning in the home: overview of systematic reviews and a systematic review of primary studies

Unintentional poisoning is a significant child public health problem. This systematic overview of reviews, supplemented with a systematic review of recently published primary studies synthesizes evidence on non-legislative interventions to reduce childhood poisonings in the home with particular reference to interventions that could be implemented by Children's Centres in England or community health or social care services in other high income countries. Thirteen systematic reviews, two meta-analyses and 47 primary studies were identified. The interventions most commonly comprised education, provision of cupboard/drawer locks, and poison control centre (PCC) number stickers. Meta-analyses and primary studies provided evidence that interventions improved poison prevention practices. Twenty eight per cent of studies reporting safe medicine storage (OR from meta-analysis 1.57, 95% CI 1.22–2.02), 23% reporting safe storage of other products (OR from meta-analysis 1.63, 95% CI 1.22–2.17) and 46% reporting availability of PCC numbers (OR from meta-analysis 3.67, 95% CI 1.84–7.33) demonstrated significant effects favouring the intervention group. There was a lack of evidence that interventions reduced poisoning rates. Parents should be provided with poison prevention education, cupboard/drawer locks and emergency contact numbers to use in the event of a poisoning. Further research is required to determine whether improving poison prevention practices reduces poisoning rates

Inducing safer oblique trees without costs

Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than in the other class. Several authors have tackled this problem by developing cost-sensitive decision tree learning algorithms or have suggested ways of changing the distribution of training examples to bias the decision tree learning process so as to take account of costs. A prerequisite for applying such algorithms is the availability of costs of misclassification. Although this may be possible for some applications, obtaining reasonable estimates of costs of misclassification is not easy in the area of safety. This paper presents a new algorithm for applications where the cost of misclassifications cannot be quantified, although the cost of misclassification in one class is known to be significantly higher than in another class. The algorithm utilizes linear discriminant analysis to identify oblique relationships between continuous attributes and then carries out an appropriate modification to ensure that the resulting tree errs on the side of safety. The algorithm is evaluated with respect to one of the best known cost-sensitive algorithms (ICET), a well-known oblique decision tree algorithm (OC1) and an algorithm that utilizes robust linear programming

The Transporter Classification Database: recent advances

The Transporter Classification Database (TCDB), freely accessible at http://www.tcdb.org, is a relational database containing sequence, structural, functional and evolutionary information about transport systems from a variety of living organisms, based on the International Union of Biochemistry and Molecular Biology-approved transporter classification (TC) system. It is a curated repository for factual information compiled largely from published references. It uses a functional/phylogenetic system of classification, and currently encompasses about 5000 representative transporters and putative transporters in more than 500 families. We here describe novel software designed to support and extend the usefulness of TCDB. Our recent efforts render it more user friendly, incorporate machine learning to input novel data in a semiautomatic fashion, and allow analyses that are more accurate and less time consuming. The availability of these tools has resulted in recognition of distant phylogenetic relationships and tremendous expansion of the information available to TCDB users

A survey of cost-sensitive decision tree induction algorithms

The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field

ROC curves in cost space

The final publication is available at Springer via http://dx.doi.org/10.1007/s10994-013-5328-9ROC curves and cost curves are two popular ways of visualising classifier performance, finding appropriate thresholds according to the operating condition, and deriving useful aggregated measures such as the area under the ROC curve (AUC) or the area under the optimal cost curve. In this paper we present new findings and connections between ROC space and cost space. In particular, we show that ROC curves can be transferred to cost space by means of a very natural threshold choice method, which sets the decision threshold such that the proportion of positive predictions equals the operating condition. We call these new curves rate-driven curves, and we demonstrate that the expected loss as measured by the area under these curves is linearly related to AUC. We show that the rate-driven curves are the genuine equivalent of ROC curves in cost space, establishing a point-point rather than a point-line correspondence. Furthermore, a decomposition of the rate-driven curves is introduced which separates the loss due to the threshold choice method from the ranking loss (Kendall τ distance). We also derive the corresponding curve to the ROC convex hull in cost space; this curve is different from the lower envelope of the cost lines, as the latter assumes only optimal thresholds are chosen.We would like to thank the anonymous referees for their helpful comments. This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, the COST-European Cooperation in the field of Scientific and Technical Research IC0801 AT, and the REFRAME project granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA), and funded by the Engineering and Physical Sciences Research Council in the UK and the Ministerio de Economia y Competitividad in Spain.Hernández Orallo, J.; Flach ., P.; Ferri Ramírez, C. (2013). ROC curves in cost space. Machine Learning. 93(1):71-91. https://doi.org/10.1007/s10994-013-5328-9S7191931Adams, N., & Hand, D. (1999). Comparing classifiers when the misallocation costs are uncertain. Pattern Recognition, 32(7), 1139–1147.Chang, J., & Yap, C. (1986). A polynomial solution for the potato-peeling problem. Discrete & Computational Geometry, 1(1), 155–182.Drummond, C., & Holte, R. (2000). Explicitly representing expected cost: an alternative to ROC representation. In Knowl. discovery & data mining (pp. 198–207).Drummond, C., & Holte, R. (2006). Cost curves: an improved method for visualizing classifier performance. Machine Learning, 65, 95–130.Elkan, C. (2001). The foundations of cost-sensitive learning. In B. Nebel (Ed.), Proc. of the 17th intl. conf. on artificial intelligence (IJCAI-01) (pp. 973–978).Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.Fawcett, T., & Niculescu-Mizil, A. (2007). PAV and the ROC convex hull. Machine Learning, 68(1), 97–106.Flach, P. (2003). The geometry of ROC space: understanding machine learning metrics through ROC isometrics. In Machine learning, proceedings of the twentieth international conference (ICML 2003) (pp. 194–201).Flach, P., Hernández-Orallo, J., & Ferri, C. (2011). A coherent interpretation of AUC as a measure of aggregated classification performance. In Proc. of the 28th intl. conference on machine learning, ICML2011.Frank, A., & Asuncion, A. (2010). UCI machine learning repository. http://archive.ics.uci.edu/ml .Hand, D. (2009). Measuring classifier performance: a coherent alternative to the area under the ROC curve. Machine Learning, 77(1), 103–123.Hernández-Orallo, J., Flach, P., & Ferri, C. (2011). Brier curves: a new cost-based visualisation of classifier performance. In Proceedings of the 28th international conference on machine learning, ICML2011.Hernández-Orallo, J., Flach, P., & Ferri, C. (2012). A unified view of performance metrics: translating threshold choice into expected classification loss. Journal of Machine Learning Research, 13, 2813–2869.Kendall, M. G. (1938). A new measure of rank correlation. Biometrika, 30(1/2), 81–93. doi: 10.2307/2332226 .Swets, J., Dawes, R., & Monahan, J. (2000). Better decisions through science. Scientific American, 283(4), 82–87