A particle swarm based hybrid system for imbalances medical data sampling

Background
Medical and biological data are commonly with small sample size, missing values, and most importantly, imbalanced class distribution. In this study we propose a particle swarm based hybrid system for remedying the class imbalance problem in medical and biological data mining. This hybrid system combines the particle swarm optimization (PSO) algorithm with multiple classifiers and evaluation metrics for evaluation fusion. Samples from the majority class are ranked using multiple objectives according to their merit in compensating the class imbalance, and then combined with the minority class to form a balanced dataset.

Results
One important finding of this study is that different classifiers and metrics often provide different evaluation results. Nevertheless, the proposed hybrid system demonstrates consistent improvements over several alternative methods with three different metrics. The sampling results also demonstrate good generalization on different types of classification algorithms, indicating the advantage of information fusion applied in the hybrid system.

Conclusion
The experimental results demonstrate that unlike many currently available methods which often perform unevenly with different datasets the proposed hybrid system has a better generalization property which alleviates the method-data dependency problem. From the biological perspective, the system provides indication for further investigation of the highly ranked samples, which may result in the discovery of new conditions or disease subtypes.

Comparing probability and non-probability sampling methods in ecstasy research : implications for the internet as a research tool

The usage of Ecstasy and related drug (ERD) has increasingly been the focus of epidemiological and other public health-related research. One of the more promising methods is the use of the Internet as a recruitment and survey tool.However, there remain methodological concerns and questions about representativeness. Three samples of ERD users in Melbourne, Australia surveyed in 2004 are compared in terms of a number of key demographic and drug use variables. The Internet, face-to-face, and probability
sampling methods appear to access similar but not identical groups of ERD users. Implications and limitations of the study are noted and future research is recommended.

A comparison of the effectiveness of camera trapping and live trapping for sampling terrestrial small-mammal communities

Context. There is an increasing reliance on the use of camera-trap technologies for surveys of medium to large terrestrial mammals. Camera trapping may, however, also have significant applications for broad-scale surveys of small mammals.
Aims. The present study aims to compare results from camera-trapping surveys to those of the more traditional live trapping techniques. Specifically, it aims to test the effectiveness of the techniques for detecting species, and the cost effectiveness of both approaches.
Methods. Surveys were conducted across 36 sites in the Grampians National Park, Victoria, Australia, between April and July 2009. At each site, independent surveys were conducted for small mammals by using a combination of Elliot and cage trapping, then camera trapping. Results for the two different approaches were compared for both their ability to generate small-mammal presence data and their cost effectiveness.
Key results. Camera-trapping surveys of 36 sites in the Grampians National Park compared favourably with those of live trapping surveys. Similar species were detected across the sites, and camera trapping was a considerably more cost effective than live trapping.
Conclusions. Camera-trapping surveys of small terrestrial mammals may provide a new and cost-effective technique for surveying terrestrial small mammals. This is particularly the case when presence data are the main requirement of the survey, with no requirement to capture and tag animals.
Implications. Given the cost-effective nature of camera trapping, there is potential to use this approach to increase the level of replication and spatial coverage of small-mammal surveys. Improving the replication and spatial coverage of studies has the potential to significantly increase the scope of research questions that can be asked, thus providing the potential to improve wildlife management.

Estimating process capability index Cpm using a bootstrap sequential sampling procedure

Construction of a confidence interval for process capability index CPM is often based on a normal approximation with fixed sample size. In this article, we describe a different approach in constructing a fixed-width confidence interval for process capability index CPM with a preassigned accuracy by using a combination of bootstrap and sequential sampling schemes. The optimal sample size required to achieve a preassigned confidence level is obtained using both two-stage and modified two-stage sequential procedures. The procedure developed is also validated using an extensive simulation study.

Are most samples of animals systematically biased? Consistent individual trait differences bias samples despite random sampling

Sampling animals from the wild for study is something nearly every biologist has done, but despite our best efforts to obtain random samples of animals, ‘hidden’ trait biases may still exist. For example, consistent behavioral traits can affect trappability/catchability, independent of obvious factors such as size and gender, and these traits are often correlated with other repeatable physiological and/or life history traits. If so, systematic sampling bias may exist for any of these traits. The extent to which this is a problem, of course, depends on the magnitude of bias, which is presently unknown because the underlying trait distributions in populations are usually unknown, or unknowable. Indeed, our present knowledge about sampling bias comes from samples (not complete population censuses), which can possess bias to begin with. I had the unique opportunity to create naturalized populations of fish by seeding each of four small fishless lakes with equal densities of slow-, intermediate-, and fast-growing fish. Using sampling methods that are not size-selective, I observed that fast-growing fish were up to two-times more likely to be sampled than slower-growing fish. This indicates substantial and systematic bias with respect to an important life history trait (growth rate). If correlations between behavioral, physiological and life-history traits are as widespread as the literature suggests, then many animal samples may be systematically biased with respect to these traits (e.g., when collecting animals for laboratory use), and affect our inferences about population structure and abundance. I conclude with a discussion on ways to minimize sampling bias for particular physiological/behavioral/life-history types within animal populations.

The sampling and remix dilemma: what is the role of moral rights in the encouragement and regulation of derivative creativity

The borrowing and rearrangement of musical content, especially in the digital context, raises difficult questions for copyright law. There is significant community support for a loosening of the restrictions on the derivative (and particularly creative) use of copyright material. Law reform is called for. This paper discusses the possible introduction of a new exception to copyright infringement but notes that in the drafting of any such exception not only the economic rights but also the moral rights of the originating author need to be taken into account.

Statistical approaches to testing the relationships of the built environment with resident-level physical activity behavior and health outcomes in cross-sectional studies with cluster sampling

To achieve valid conclusions, studies exploring associations of the built environment with residents' physical activity and health-related outcomes need to employ statistical approaches accounting for clustered data. This article discusses the following main statistical approaches: analysis of covariance, regression models with robust standard errors, generalized estimating equations, and multilevel generalized linear models. The choice of a statistical method depends on the characteristics of the study and research questions. While the first three approaches are employed to account for clustering in the data, multilevel models can also help unravel more substantive issues within a social ecological theoretical framework of health behavior.