A novel consistent random forest framework: Bernoulli random forests

© 2012 IEEE. Random forests (RFs) are recognized as one type of ensemble learning method and are effective for the most classification and regression tasks. Despite their impressive empirical performance, the theory of RFs has yet been fully proved. Several theoretically guaranteed RF variants have been presented, but their poor practical performance has been criticized. In this paper, a novel RF framework is proposed, named Bernoulli RFs (BRFs), with the aim of solving the RF dilemma between theoretical consistency and empirical performance. BRF uses two independent Bernoulli distributions to simplify the tree construction, in contrast to the RFs proposed by Breiman. The two Bernoulli distributions are separately used to control the splitting feature and splitting point selection processes of tree construction. Consequently, theoretical consistency is ensured in BRF, i.e., the convergence of learning performance to optimum will be guaranteed when infinite data are given. Importantly, our proposed BRF is consistent for both classification and regression. The best empirical performance is achieved by BRF when it is compared with state-of-the-art theoretical/consistent RFs. This advance in RF research toward closing the gap between theory and practice is verified by the theoretical and experimental studies in this paper

Skin Lesion Analysis Toward Melanoma Detection Using Deep Learning Techniques

In the last few years, a great attention was paid to the deep learning Techniques used for image analysis because of their ability to use machine learning techniques to transform input data into high level presentation. For the sake of accurate diagnosis, the medical field has a steadily growing interest in such technology especially in the diagnosis of melanoma. These deep learning networks work through making coarse segmentation, conventional filters and pooling layers. However, this segmentation of the skin lesions results in image of lower resolution than the original skin image. In this paper, we present deep learning based approaches to solve the problems in skin lesion analysis using a dermoscopic image containing skin tumor. The proposed models are trained and evaluated on standard benchmark datasets from the International Skin Imaging Collaboration (ISIC) 2018 Challenge. The proposed method achieves an accuracy of 96.67% for the validation set .The experimental tests carried out on a clinical dataset show that the classification performance using deep learning-based features performs better than the state-of-the-art technique

Skin Lesion Analysis Toward Melanoma Detection Using Deep Learning Techniques

In the last few years, a great attention was paid to the deep learning Techniques used for image analysis because of their ability to use machine learning techniques to transform input data into high level presentation. For the sake of accurate diagnosis, the medical field has a steadily growing interest in such technology especially in the diagnosis of melanoma. These deep learning networks work through making coarse segmentation, conventional filters and pooling layers. However, this segmentation of the skin lesions results in image of lower resolution than the original skin image. In this paper, we present deep learning based approaches to solve the problems in skin lesion analysis using a dermoscopic image containing skin tumor. The proposed models are trained and evaluated on standard benchmark datasets from the International Skin Imaging Collaboration (ISIC) 2018 Challenge. The proposed method achieves an accuracy of 96.67% for the validation set .The experimental tests carried out on a clinical dataset show that the classification performance using deep learning-based features performs better than the state-of-the-art technique

Reliability Models Applied to Mobile Applications

Smartphones have become the most used electronic devices. They carried out most of the functionalities of desktops, allowing various useful applications that suit the users’ needs. Therefore, instead of the operator, the user has become the number one controller of the device and its applications and thus its reliability becomes an emergent need. We aim to investigate and evaluate the efficacy of Software Reliability Growth Models (SRGMs) when applied to Smartphone application failure data and check whether they achieve the same success as in the desktop/laptop area. We selected three of the most used SRGMs and applied them to three different Smartphone applications. None of the selected models were able to account for the data satisfactorily. Their failure is traced back to the specific features of mobile applications compared to desktop applications. Thus, a suitable model for Smartphone applications is still needed to improve their reliability

Exploiting past users’ interests and predictions in an active learning method for dealing with cold start in recommender systems

This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the new users to interact with the system by presenting them with a questionnaire that aims to understand their preferences to the related items. In this paper, we propose an active learning technique that exploits past users’ interests and past users’ predictions in order to identify the best questions to ask. Our technique achieves a better performance in terms of precision (RMSE), which leads to learn the users’ preferences in less questions. The experimentations were carried out in a small and public dataset to prove the applicability for handling cold start issues

Binge Drinking: The Top 100 Cited Papers

We conducted a review to analyze the 100 most-cited studies on binge drinking (BD) in the Web of Science (WoS) database to determine their current status and the aspects that require further attention. We carried out a retrospective bibliometric analysis in January 2021. The year of publication, authors, design, subject, journal, institution and lead author's country, as well as the definition of BD, were extracted from the articles. The data on the country, year, thematic category of the journals and their rank were obtained from the Institute for Scientific Information (ISI) Journal Citation Reports 2020. The number of citations was collected from the WoS, and the h index was collected from the Scopus database. The citation density and Bradford's law were calculated. The majority of the articles were empirical quantitative studies with a cross-sectional design published between 1992 and 2013 in 49 journals. There were 306 authors, mostly English-speaking and from the USA. The definitions used to describe BD are not homogeneous. The most-cited topics were the analysis of consequences, determinants and epidemiology. There is a need to unify the definitions of BD and base them on scientific evidence. The multidisciplinary nature of BD is not well reflected in each of the thematic areas discussed in this work

A Concurrent Language for Argumentation: Preliminary Notes

While agent-based modelling languages naturally implement concurrency, the currently available languages for argumentation do not allow to explicitly model this type of interaction. In this paper we introduce a concurrent language for handling process arguing and communicating using a shared argumentation framework (reminding shared constraint store as in concurrent constraint). We introduce also basic expansions, contraction and revision procedures as main bricks for enforcement, debate, negotiation and persuasion