Positive and unlabeled learning in categorical data

International audienceIn common binary classification scenarios, the presence of both positive and negative examples in training datais needed to build an efficient classifier. Unfortunately, in many domains, this requirement is not satisfied andonly one class of examples is available. To cope with this setting, classification algorithms have been introducedthat learn from Positive and Unlabeled (PU) data. Originally, these approaches were exploited in the context ofdocument classification. Only few works address the PU problem for categorical datasets. Nevertheless, theavailable algorithms are mainly based on Naive Bayes classifiers. In this work we present a new distance basedPU learning approach for categorical data: Pulce. Our framework takes advantage of the intrinsic relationshipsbetween attribute values and exceeds the independence assumption made by Naive Bayes. Pulce, in fact,leverages on the statistical properties of the data to learn a distance metric employed during the classificationtask. We extensively validate our approach over real world datasets and demonstrate that our strategy obtainsstatistically significant improvements w.r.t. state-of-the-art competitors

New adhesive traps to monitor urban mosquitoes with a case study to assess the efficacy of insecticide control strategies in temperate areas

Background: Urban mosquitoes in temperate regions may represent a high nuisance and are associated with the risk of arbovirus transmission. Common practices to reduce this burden, at least in Italian highly infested urban areas, imply calendar-based larvicide treatments of street catch basins – which represent the main non-removable urban breeding site and/or insecticide ground spraying. The planning of these interventions, as well as the evaluation of their effectiveness, rarely benefit of adequate monitoring of the mosquito abundance and dynamics. We propose the use of adhesive traps to monitor Aedes albopictus and Culex pipiens adults and to evaluate the efficacy of insecticide-based control strategies. Methods: We designed two novel types of adhesive traps to collect adult mosquitoes visiting and/or emerging from catch basins. The Mosquito Emerging Trap (MET) was exploited to assess the efficacy of larvicide treatments. The Catch Basin Trap (CBT) was exploited together with the Sticky Trap (ST, commonly used to collect ovipositing/resting females) to monitor adults abundance in the campus of the University of Rome “Sapienza” - where catch basins were treated with Insect Growth Regulators (IGR) bi-monthly and Low-Volume insecticide spraying were carried out before sunset - and in a nearby control area. Results: Results obtained by MET showed that, although all monitored diflubenzuron-treated catch basins were repeatedly visited by Ae. albopictus and Cx. pipiens, adult emergence was inhibited in most basins. Results obtained by ST and CBT showed a significant lower adult abundance in the treated area than in the untreated one after the second adulticide spraying, which was carried out during the major phase of Ae. albopictus population expansion in Rome. Spatial heterogeneities in the effect of the treatments were also revealed. Conclusions: The results support the potential of the three adhesive traps tested in passively monitoring urban mosquito adult abundance and seasonal dynamics and in assessing the efficacy of control measures. ST showed higher specificity for Ae. albopictus and CBT for Cx. pipiens. The results also provide a preliminary indication on the effectiveness of common mosquito control strategies carried out against urban mosquito in European urban areas

Représentation à base de connaissance pour une méthode de classification transductive de document multilangue

International audienceMultilingual document classification is often addressed by approaches that rely on language-specific resources (e.g., bilingual dictionaries and machine translation tools) to evaluate cross-lingual document similarities. However, the required transformations may alter the original document semantics, raising additional issues to the known difficulty of obtaining high-quality labeled datasets. To overcome such issues we propose a new framework for multilingual document classification under a transductive learning setting. We exploit a large-scale multilingual knowledge base, BabelNet, to support the modeling of different language-written documents into a common conceptual space, without requiring any language translation process. We resort to a state-of-the-art transductive learner to produce the document classification. Results on two real-world multilingual corpora have highlighted the effectiveness of the proposed document model w.r.t. document representations usually involved in multilingual and cross-lingual analysis, and the robustness of the transductive setting for multilingual document classification