@article {80,
	title = {Empirically validating software metrics for risk prediction based on intelligent methods},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2007},
	pages = {99 - 106},
	abstract = {The software systems which are related to national science and technology projects are very crucial. This kind of systems always involves high technical factors and has to spend a large amount of money, so the quality and reliability of the software deserve to be further studied. Hence, we propose to apply four intelligent classification techniques most used in data mining fields, including Bayesian belief networks (BBN), nearest neighbor (NN), rough set (RS) and decision tree (DT), to validate the usefulness of software metrics for risk prediction. Results show that comparing with metrics such as Lines of code (LOC) and Cyclomatic complexity (V(G)) which are traditionally used for risk prediction, Halstead program difficulty (D), Number of executable statements (EXEC) and Halstead program volume (V) are the more effective metrics as risk predictors. By analyzing obtained results we also found that BBN was more effective than the other three methods in risk prediction.},
	keywords = {Bayesian belief networks, Decision tree, Nearest neighbor, Risk prediction, Software metrics},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-70350695759\&partnerID=40\&md5=f939c6d69c641cbeee0b6e64381ba65a},
	author = {Xu, Z. and Zheng, X. and Guo, P.}
}
@article {87,
	title = {Multi-source remote sensing classification based on Mallat fusion and residual error feature selection},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Export Date: 10 July 2014},
	month = {2007},
	pages = {130 - 137},
	abstract = {Classification of multi-source remote sensing images has been studied for decades, and many methods have been proposed or improved. Most of these studies focus on how to improve the classifiers in order to obtain higher classification accuracy. However, as we know, even if the most promising method such as neural network, its performance not only depends on the classifier itself, but also has relation with the training pattern (i.e. features). On consideration of this aspect, we propose an approach to feature selection and classification of multi-source remote sensing images based on Mallat fusion and residual error in this paper. Firstly, the fusion of multi-source images can provide a fused image which is more preferable for classification. And then a featureselection scheme approach based on fused image is proposed, which is to select effective subsets of features as inputs of a classifier by taking into account the residual error associated with each land-cover class. In addition, a classification technique base on selected features by using a feed-forward neural network is investigated. The results of computer experiments carried out on a multisource data set confirm the validity of the proposed approach.},
	keywords = {Feature selection, Mallet fusion, Multi-source classification, Neural network, Residual error},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-70350668378\&partnerID=40\&md5=8e01a98aa0645ce6934545475591db77},
	author = {Cao, D. and Yin, Q. and Guo, P.}
}
