Evaluation of random forests on large-scale classification problems using a bag-of-visual-words representation

Random Forest is a very efficient classification method that has shown success in tasks like image segmentation or object detection, but has not been applied yet in large-scale image classification scenarios using a Bag-of-Visual-Words representation. In this work we evaluate the performance of Random Forest on the ImageNet dataset, and compare it to standard approaches in the state-of-the-art.Peer ReviewedPostprint (author’s final draft

Evaluation of random forests on large-scale classification problems using a bag-of-visual-words representation

Trabajo presentado a la 2nd KES International Conference on Innovation in Medicine and Healthcare (InMed-14), celebrada en San Sebastian (España) del 9 al 11 de julio de 2014.Random Forest is a very efficient classification method that has shown success in tasks like image segmentation or object detection, but has not been applied yet in large-scale image classification scenarios using a Bag-of-Visual-Words representation. In this work we evaluate the performance of Random Forest on the ImageNet dataset, and compare it to standard approaches in the state-of-the-art.This research is partially funded by the Spanish Ministry of Science and Innovation under project DPI2011-27510, by the CSIC project CINNOVA (201150E088) and by the ERA-Net CHISTERA project ViSen PCIN-2013-047. A. Ramisa worked under CSIC/FSE JAE-Doc grant.Peer Reviewe

Enhancing Parkinson’s Disease Prediction Using Machine Learning and Feature Selection Methods

Several millions of people suffer from Parkinson’s disease globally. Parkinson’s affects about 1% of people over 60 and its symptoms increase with age. The voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners, but which could be analyzed using recorded speech signals. With the huge advancements of technology, the medical data has increased dramatically, and therefore, there is a need to apply data mining and machine learning methods to extract new knowledge from this data. Several classification methods were used to analyze medical data sets and diagnostic problems, such as Parkinson’s Disease (PD). In addition, to improve the performance of classification, feature selection methods have been extensively used in many fields. This paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and wrapper-based. The dataset includes 240 recodes with 46 acoustic features extracted from 3 voice recording replications for 80 patients. The experimental results showed improvements when wrapper-based features selection method was used with KNN classifier with accuracy of 88.33%. The best obtained results were compared with other studies and it was found that this study provides comparable and superior results

Malware threats and detection for industrial mobile-IoT networks

Industrial IoT networks deploy heterogeneous IoT devices to meet a wide range of user requirements. These devices are usually pooled from private or public IoT cloud providers. A significant number of IoT cloud providers integrate smartphones to overcome the latency of IoT devices and low computational power problems. However, the integration of mobile devices with industrial IoT networks exposes the IoT devices to significant malware threats. Mobile malware is the highest threat to the security of IoT data, user\u27s personal information, identity, and corporate/financial information. This paper analyzes the efforts regarding malware threats aimed at the devices deployed in industrial mobile-IoT networks and related detection techniques. We considered static, dynamic, and hybrid detection analysis. In this performance analysis, we compared static, dynamic, and hybrid analyses on the basis of data set, feature extraction techniques, feature selection techniques, detection methods, and the accuracy achieved by these methods. Therefore, we identify suspicious API calls, system calls, and the permissions that are extracted and selected as features to detect mobile malware. This will assist application developers in the safe use of APIs when developing applications for industrial IoT networks

Evaluation of random forests on large-scale classification problems using a bag-of-visual-words representation

Treball presentat en Facultat d'Informàtica de Barcelona - Enginyeria Informàtica (UPC) i realitzat en L’Institut de Robòtica i Informàtica Industrial (IRI), que és un Centre d’Investigació de la Universitat Politècnica de Catalunya (UPC) i el Consell Superior d’Investigacions Científiques (CSIC).En aquest projecte hem avaluat els Random Forests en el context de classificació d'imatges a gran escala, concretament en els conjunts de dades d'ImageNet LSVRC'10 i Caltech-256. També hem realitzat una comparativa de rendiment del mètode Random Forests amb els mètodes OvR-SVM i ECBND.Peer Reviewe

Evaluation of random forests on large-scale classification problems using a bag-of-visual-words representation

Random Forest is a very efficient classification method that has shown success in tasks like image segmentation or object detection, but has not been applied yet in large-scale image classification scenarios using a Bag-of-Visual-Words representation. In this work we evaluate the performance of Random Forest on the ImageNet dataset, and compare it to standard approaches in the state-of-the-art.Peer Reviewe