A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

With the proliferation of high-dimensional datasets across many application domains in
recent years, feature selection has become an important data mining task due to its …

In software maintenance, severity prediction on defect reports is an emerging issue
obtaining research attention due to the considerable triaging cost. In the past research work …

CONTEXT: Previous work by researchers on 3years of early data for an Eclipse product has
identified some predictors of failure-prone files that work well. Eclipse has also been used …

Software quality modeling is the process of using software metrics from previous iterations of
development to locate potentially faulty modules in current under-development code. This …

Software metric (feature) selection is an important pre-processing step before building
software defect prediction models. Although much research has been done analyzing the …

Software metrics play an important role in Software Development Life Cycle (SDLC). In this
paper we have tried to find the correlation between different software metrics. The research …

Gene expression data often need to be classified into classes or grouped into clusters for
further analysis, using different machine learning techniques and an important pre …

In large software projects, software quality prediction is an important aspect of the
development cycle to help focus quality assurance efforts on the modules most likely to …

 Abstract—Software Defect Data (SDD) are used to build defect prediction models for
software quality assurance. Existing work employs feature selection to eliminate irrelevant …