this paper presents a data envelopment analysis (dea) model combined with bootstrapping to assess performance of one of the data mining algorithms. we applied a two-step process for performance productivity analysis of insurance branches within a case study. first, using a dea model, the study analyzes the productivity of eighteen decision-making units (dmus). using a malmquist index, dea deter...

Objectives: This study examined main effects of controllability and interactive effects of controllability and generalisability attributions upon self-efficacy. Design: A cross-sectional study design was employed with pre-competition self-efficacy assessed at least one week prior to attributions and subsequent self-efficacy. Method: Participants (N 1⁄4 360; mean age 21.64, SD 1⁄4 6.96 years) co...

Classification is one of the most important research contents in data mining and traditional classification methods are relatively mature, when dealing with well-balanced data they can make good performances. But in real world the data is usually imbalanced, that is, most of the data are in majority class and little data are in minority class. Imbalanced data set cause the deduction of the prec...

In this research, we present a hierarchical Data Envelopment Analysis (DEA) methodology for competitiveness analysis. This methodology takes into account the heterogeneity of the decision making units (DMUs) as well as the diversity of the comparison criteria. We propose to homogenize the DMUs by grouping them hierarchically, which permits a better identification and definition of the criteria ...

Grid data sources may have schemaand data-level conflicts that need to be addressed using data transformation and integration technologies not supported by the current generation of Grid data access and querying middleware. We present an architecture that combines Grid data access and distributed querying with fine-grained data transformation/integration technologies, and the results of a query...

This paper discusses the approach to curation of the MuMe Weekend (held in Sydney, Australia, 2013) and the experience the authors gained from hosting the event. We identify open challenges arising from this applied demonstration of musically metacreative systems. Identified challenges are not technical, but rather methodological, concerning pragmatic aspects of presenting and collaboratively i...

Provenance about workflow executions and data derivations in scientific applications help estimate data quality, track resources, and validate in silico experiments. The Karma provenance framework provides a means to collect workflow, process, and data provenance from data-driven scientific workflows and is used in the Linked Environments for Atmospheric Discovery (LEAD) project. This paper pre...