Random forests with random projections of the output space for high dimensional multi-label classification

We adapt the idea of random projections applied to the output space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can be reduced without affecting computational complexity and accuracy of predictions. We also show that random output space projections may be used in order to reach different bias-variance tradeoffs, over a broad panel of benchmark problems, and that this may lead to improved accuracy while reducing significantly the computational burden of the learning stage

On Aggregation in Ensembles of Multilabel Classifiers

While a variety of ensemble methods for multilabel classification have been proposed in the literature, the question of how to aggregate the predictions of the individual members of the ensemble has received little attention so far. In this paper, we introduce a formal framework of ensemble multilabel classification, in which we distinguish two principal approaches: "predict then combine" (PTC), where the ensemble members first make loss minimizing predictions which are subsequently combined, and "combine then predict" (CTP), which first aggregates information such as marginal label probabilities from the individual ensemble members, and then derives a prediction from this aggregation. While both approaches generalize voting techniques commonly used for multilabel ensembles, they allow to explicitly take the target performance measure into account. Therefore, concrete instantiations of CTP and PTC can be tailored to concrete loss functions. Experimentally, we show that standard voting techniques are indeed outperformed by suitable instantiations of CTP and PTC, and provide some evidence that CTP performs well for decomposable loss functions, whereas PTC is the better choice for non-decomposable losses.Comment: 14 pages, 2 figure

Ontology of core data mining entities

In this article, we present OntoDM-core, an ontology of core data mining entities. OntoDM-core defines themost essential datamining entities in a three-layered ontological structure comprising of a specification, an implementation and an application layer. It provides a representational framework for the description of mining structured data, and in addition provides taxonomies of datasets, data mining tasks, generalizations, data mining algorithms and constraints, based on the type of data. OntoDM-core is designed to support a wide range of applications/use cases, such as semantic annotation of data mining algorithms, datasets and results; annotation of QSAR studies in the context of drug discovery investigations; and disambiguation of terms in text mining. The ontology has been thoroughly assessed following the practices in ontology engineering, is fully interoperable with many domain resources and is easy to extend

Timed written picture naming in 14 European languages

We describe the Multilanguage Written Picture Naming Dataset. This gives trial-level data and time and agreement norms for written naming of the 260 pictures of everyday objects that compose the colorized Snodgrass and Vanderwart picture set (Rossion & Pourtois in Perception, 33, 217-236, 2004). Adult participants gave keyboarded responses in their first language under controlled experimental conditions (N = 1,274, with subsamples responding in Bulgarian, Dutch, English, Finnish, French, German, Greek, Icelandic, Italian, Norwegian, Portuguese, Russian, Spanish, and Swedish). We measured the time to initiate a response (RT) and interkeypress intervals, and calculated measures of name and spelling agreement. There was a tendency across all languages for quicker RTs to pictures with higher familiarity, image agreement, and name frequency, and with higher name agreement. Effects of spelling agreement and effects on output rates after writing onset were present in some, but not all, languages. Written naming therefore shows name retrieval effects that are similar to those found in speech, but our findings suggest the need for cross-language comparisons as we seek to understand the orthographic retrieval and/or assembly processes that are specific to written output

Common Variants in the COL4A4 Gene Confer Susceptibility to Lattice Degeneration of the Retina

Lattice degeneration of the retina is a vitreoretinal disorder characterized by a visible fundus lesion predisposing the patient to retinal tears and detachment. The etiology of this degeneration is still uncertain, but it is likely that both genetic and environmental factors play important roles in its development. To identify genetic susceptibility regions for lattice degeneration of the retina, we performed a genome-wide association study (GWAS) using a dense panel of 23,465 microsatellite markers covering the entire human genome. This GWAS in a Japanese cohort (294 patients with lattice degeneration and 294 controls) led to the identification of one microsatellite locus, D2S0276i, in the collagen type IV alpha 4 (COL4A4) gene on chromosome 2q36.3. To validate the significance of this observation, we evaluated the D2S0276i region in the GWAS cohort and in an independent Japanese cohort (280 patients and 314 controls) using D2S0276i and 47 single nucleotide polymorphisms covering the region. The strong associations were observed in D2S0276i and rs7558081 in the COL4A4 gene (Pc = 5.8×10−6, OR = 0.63 and Pc = 1.0×10−5, OR = 0.69 in a total of 574 patients and 608 controls, respectively). Our findings suggest that variants in the COL4A4 gene may contribute to the development of lattice degeneration of the retina

Ceramic Microbial Fuel Cells Stack: Power generation in standard and supercapacitive mode

© 2018 The Author(s). In this work, a microbial fuel cell (MFC) stack containing 28 ceramic MFCs was tested in both standard and supercapacitive modes. The MFCs consisted of carbon veil anodes wrapped around the ceramic separator and air-breathing cathodes based on activated carbon catalyst pressed on a stainless steel mesh. The anodes and cathodes were connected in parallel. The electrolytes utilized had different solution conductivities ranging from 2.0 mScm-1 to 40.1 mScm-1, simulating diverse wastewaters. Polarization curves of MFCs showed a general enhancement in performance with the increase of the electrolyte solution conductivity. The maximum stationary power density was 3.2 mW (3.2 Wm-3) at 2.0 mScm-1 that increased to 10.6 mW (10.6 Wm-3) at the highest solution conductivity (40.1 mScm-1). For the first time, MFCs stack with 1 L operating volume was also tested in supercapacitive mode, where full galvanostatic discharges are presented. Also in the latter case, performance once again improved with the increase in solution conductivity. Particularly, the increase in solution conductivity decreased dramatically the ohmic resistance and therefore the time for complete discharge was elongated, with a resultant increase in power. Maximum power achieved varied between 7.6 mW (7.6 Wm-3) at 2.0 mScm-1 and 27.4 mW (27.4 Wm-3) at 40.1 mScm-1

Mosaicking and enhancement of slit lamp biomicroscopic fundus images

AIMS—To process video slit lamp biomicroscopic fundus image sequences in order to generate wide field, high quality fundus image montages which might be suitable for photodocumentation.
METHODS—Slit lamp biomicroscopic fundus examination was performed on human volunteers with a contact or non-contact lens. A stock, charge coupled device camera permitted image capture and storage of the image sequence at 30 frames per second. Acquisition time was approximately 30 seconds. Individual slit lamp biomicroscope fundus image frames were aligned and blended with custom developed software.
RESULTS—The developed algorithms allowed for highly accurate alignment and blending of partially overlapping slit lamp biomicroscopic fundus images to generate a seamless, high quality, wide field montage.
CONCLUSIONS—Video image acquisition and processing algorithms allow for mosaicking and enhancement of slit lamp biomicroscopic fundus images. The improved quality and wide field of view may confer suitability for inexpensive, real time photodocumentation of disc and macular abnormalities.