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

The long-term optical behavior of MRK421

All data available in B band for the BL Lac object MRK421 from 22 publications are used to construct a historical light curve, dating back to 1900. It is found that the light curve is very complicated and consists of a set of outbursts with very large duration. The brightness of MRK421 varies from 11.6 magnitude to more than 16 magnitude. Analyses with Jurkevich method of computing period of cyclic phenomena reveal in the light curve two kinds of behaviors. The first one is non-periodic with rapid, violent variations in intensity on time scales of hours to days. The second one is periodic with a possible period of $23.1\pm 1.1$ years. Another possible period of $15.3\pm 0.7$ years is not very significant. We have tested the robustness of the Jurkevich method. The period of about one year found in the light curves of MRK421 and of other objects is a spurious period due to the method and the observing window. We try to explain the period of $23.1 \pm1.1$ years under the thermal instability of a slim accretion disk around a massive black hole of mass of $2 *10^6 M_\odot$.Comment: Tex, 14 pages, 5 Postscript figures. Accepted for publication in A&A Supplement Serie