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E. K. Tang, P. N. Suganthan and X. Yao, “Gene Selection Algorithms for Microarray Data Based on Least Square Support Vector Machine,” BMC Bioinformatics, Vol. 7, No. 95, 2006. doi:10.1186/1471-2105-7-95

has been cited by the following article:

• TITLE: Mapping Software Metrics to Module Complexity: A Pattern Classification Approach

  AUTHORS: Nick John Pizzi

  KEYWORDS: Software Metrics, Pattern Classification, Feature Selection, Software Complexity

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.4 No.7, July 12, 2011

  ABSTRACT: A desirable software engineering goal is the prediction of software module complexity (a qualitative concept) using automatically generated software metrics (quantitative measurements). This goal may be couched in the language of pattern classification; namely, given a set of metrics (a pattern) for a software module, predict the class (level of complexity) to which the module belongs. To find this mapping from metrics to complexity, we present a classification strategy, stochastic metric selection, to determine the subset of software metrics that yields the greatest predictive power with respect to module complexity. We demonstrate the effectiveness of this strategy by empirically evaluating it using a publicly available dataset of metrics compiled from a medical imaging system and comparing the prediction results against several classification system benchmarks.