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In this paper, we introduced a new approach of combining multiple classifiers in a heterogeneous ensemble system. Instead of using numerical membership values when combining, we constructed interval membership values for each class prediction from the meta-data of observation by using the concept of information granule. In the proposed method, the uncertainty (diversity) of the predictions produced by the base classifiers is quantified by the interval-based information granules. The decision model is then generated by considering both bound and length of the intervals. Extensive experimentation using the UCI datasets has demonstrated the superior performance of our algorithm over other algorithms including six fixed combining methods, one trainable combining method, AdaBoost, bagging, and random subspace. [ABSTRACT FROM AUTHOR]

Porokeratoma is a rare, relatively newly described and still unclear entity. Here, we describe the case of a 52-year-old male patient who presented with four well-defined, verrucous and hyperkeratotic lesions. Microscopically, one of the lesions showed acanthopapillomatosis overlying compact orthokeratosis. Prominent broad and confluent cornoid lamellae were present, with no granular layer and some dyskeratotic keratinocytes. PCR sequencing and in situ hybridization revealed the presence of human papillomavirus (HPV) type 16 in the lesion. The association of porokeratoma and HPV infection has not previously been reported. © 2015 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]