01700nas a2200217   4500008004100000245009300041210006900134260000900203300001300212490000600225520098800231653002901219653001801248653002101266653002001287653002101307100001101328700001401339700001201353856011701365       2007                        eng d00aEmpirically validating software metrics for risk prediction based on intelligent methods0 aEmpirically validating software metrics for risk prediction base  c2007  a99 - 1060 v53 aThe 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.10aBayesian belief networks10aDecision tree10aNearest neighbor10aRisk prediction10aSoftware metrics1 aXu, Z.1 aZheng, X.1 aGuo, P.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-70350695759&partnerID=40&md5=f939c6d69c641cbeee0b6e64381ba65a01961nas a2200217   4500008004100000245010700041210006900148260000900217300001400226490000600240520123400246653002201480653001801502653003201520653001901552653001901571100001201590700001201602700001201614856011701626       2007                        eng d00aMulti-source remote sensing classification based on Mallat fusion and residual error feature selection0 aMultisource remote sensing classification based on Mallat fusion  c2007  a130 - 1370 v53 aClassification 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.10aFeature selection10aMallet fusion10aMulti-source classification10aNeural network10aResidual error1 aCao, D.1 aYin, Q.1 aGuo, P.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-70350668378&partnerID=40&md5=8e01a98aa0645ce6934545475591db77