Two-stage hybrid feature selection algorithms for diagnosing erythemato-squamous diseases

This paper proposes two-stage hybrid feature selection algorithms to build the stable and efficient diagnostic models where a new accuracy measure is introduced to assess the models. The two-stage hybrid algorithms adopt Support Vector Machines (SVM) as a classification tool, and the extended Sequential Forward Search (SFS), Sequential Forward Floating Search (SFFS), and Sequential Backward Floating Search (SBFS), respectively, as search strategies, and the generalized F-score (GF) to evaluate the importance of each feature. The new accuracy measure is used as the criterion to evaluated the performance of a temporary SVM to direct the feature selection algorithms. These hybrid methods combine the advantages of filters and wrappers to select the optimal feature subset from the original feature set to build the stable and efficient classifiers. To get the stable, statistical and optimal classifiers, we conduct 10-fold cross validation experiments in the first stage; then we merge the 10 selected feature subsets of the 10-cross validation experiments, respectively, as the new full feature set to do feature selection in the second stage for each algorithm. We repeat the each hybrid feature selection algorithm in the second stage on the one fold that has got the best result in the first stage. Experimental results show that our proposed two-stage hybrid feature selection algorithms can construct efficient diagnostic models which have got better accuracy than that built by the corresponding hybrid feature selection algorithms without the second stage feature selection procedures. Furthermore our methods have got better classification accuracy when compared with the available algorithms for diagnosing erythemato-squamous diseases

Possible discovery of the r-process characteristics in the abundances of metal-rich barium stars

We study the abundance distributions of a sample of metal-rich barium stars provided by Pereira et al. (2011) to investigate the s- and r-process nucleosynthesis in the metal-rich environment. We compared the theoretical results predicted by a parametric model with the observed abundances of the metal-rich barium stars. We found that six barium stars have a significant r-process characteristic, and we divided the barium stars into two groups: the r-rich barium stars ($C_r>5.0$, [La/Nd]\,$<0$) and normal barium stars. The behavior of the r-rich barium stars seems more like that of the metal-poor r-rich and CEMP-r/s stars. We suggest that the most possible formation mechanism for these stars is the s-process pollution, although their abundance patterns can be fitted very well when the pre-enrichment hypothesis is included. The fact that we can not explain them well using the s-process nucleosynthesis alone may be due to our incomplete knowledge on the production of Nd, Eu, and other relevant elements by the s-process in metal-rich and super metal-rich environments (see details in Pereira et al. 2011).Comment: 5 pages, 5 figures, accepted for publication in A&