Dynamic selection of the best base classifier in one versus one

Class binarization strategies decompose the original multi-class problem into several binary sub-problems. One versus One (OVO) is one of the most popular class binarization techniques, which considers every pair of classes as a different sub-problem. Usually, the same classifier is applied to every sub-problem and then all the outputs are combined by some voting scheme. In this paper we present a novel idea where for each test instance we try to assign the best classifier in each sub-problem of OVO. To do so, we have used two simple Dynamic Classifier Selection (DCS) strategies that have not been yet used in this context. The two DCS strategies use K-NN to obtain the local region of the test-instance, and the classifier that performs the best for those instances in the local region, is selected to classify the new test instance. The difference between the two DCS strategies remains in the weight of the instance. In this paper we have also proposed a novel approach in those DCS strategies. We propose to use the K-Nearest Neighbor Equality (K-NNE) method to obtain the local accuracy. K-NNE is an extension of K-NN in which all the classes are treated independently: the K nearest neighbors belonging to each class are selected. In this way all the classes take part in the final decision. We have carried out an empirical study over several UCI databases, which shows the robustness of our proposal.The work described in this paper was partially conducted within the Basque Government Research Team Grant IT313-10 and the University of the Basque Country UPV/EHU. I. Mendialdua holds a Grant from Basque Government

Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition

first_page settings Open AccessArticle Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition by Itsaso Rodríguez-Moreno * [OrcID] , José María Martínez-Otzeta [OrcID] , Basilio Sierra [OrcID] , Itziar Irigoien , Igor Rodriguez-Rodriguez and Izaro Goienetxea [OrcID] Department of Computer Science and Artificial Intelligence, University of the Basque Country, Manuel Lardizabal 1, 20018 Donostia-San Sebastián, Spain * Author to whom correspondence should be addressed. Appl. Sci. 2020, 10(22), 8075; https://doi.org/10.3390/app10228075 Received: 1 October 2020 / Revised: 30 October 2020 / Accepted: 11 November 2020 / Published: 14 November 2020 (This article belongs to the Special Issue Advanced Intelligent Imaging Technology Ⅱ) Download PDF Browse Figures Abstract Video activity recognition, despite being an emerging task, has been the subject of important research due to the importance of its everyday applications. Video camera surveillance could benefit greatly from advances in this field. In the area of robotics, the tasks of autonomous navigation or social interaction could also take advantage of the knowledge extracted from live video recording. In this paper, a new approach for video action recognition is presented. The new technique consists of introducing a method, which is usually used in Brain Computer Interface (BCI) for electroencephalography (EEG) systems, and adapting it to this problem. After describing the technique, achieved results are shown and a comparison with another method is carried out to analyze the performance of our new approach.This work has been partially funded by the Basque Government, Research Teams grant number IT900-16, ELKARTEK 3KIA project KK-2020/00049, and the Spanish Ministry of Science (MCIU), the State Research Agency (AEI), and the European Regional Development Fund (FEDER), grant number RTI2018-093337-B-I100 (MCIU/AEI/FEDER, UE). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research

Methodological contributions by means of machine learning methods for automatic music generation and classification

189 p.Ikerketa lan honetan bi gai nagusi landu dira: musikaren sorkuntza automatikoa eta sailkapena. Musikaren sorkuntzarako bertso doinuen corpus bat hartu da abiapuntu moduan doinu ulergarri berriak sortzeko gai den metodo bat sortzeko. Doinuei ulergarritasuna hauen barnean dauden errepikapen egiturek ematen dietela suposatu da, eta metodoaren hiru bertsio nagusi aurkeztu dira, bakoitzean errepikapen horien definizio ezberdin bat erabiliz.Musikaren sailkapen automatikoan hiru ataza garatu dira: generoen sailkapena, familia melodikoen taldekatzea eta konposatzaileen identifikazioa. Musikaren errepresentazio ezberdinak erabili dira ataza bakoitzerako, eta ikasketa automatikoko hainbat teknika ere probatu dira, emaitzarik hoberenak zeinek ematen dituen aztertzeko.Gainbegiratutako sailkapenaren alorrean ere binakako sailkapenaren gainean lana egin da, aurretik existitzen zen metodo bat optimizatuz. Hainbat datu baseren gainean probatu da garatutako teknika, baita konposatzaile klasikoen piezen ezaugarriez osatutako datu base batean ere

Contributions on distance-based algorithms, multi-classifier construction and pairwise classification

179 p.Aurkezten den ikerketa lan honetan saikapen atazak landu dira, non helburua,sailkapen gainbegiratuaren artearen-egoera aberastea izan den. Sailkapengainbegiratuaren zenbait estrategi analizatu dira, beraien ezaugarri etaahuleziak aztertuz. Beraz, ezaugarri positiboak mantenduz, ahuleziak hobetzekosaiakera egin da. Hau burutu ahal izateko, sailkapen gainbegiratuarenzenbait estrategi konbinatzeaz gain, zenbait bilaketa heuristiko ere erabili dira.Sailkapen gainbegiratuko 3 ikerketa lerro desberdinetan burutu dira ekarpenak.Aurkezten diren lehenengo proposamenak, K-NN algoritmoan zentratzendira, honen zenbait bertsio aurkezten direlarik. Ondoren sailkatzaileen konbinaketarekinerlazionatutako beste lan bat aurkezten da. Eta azkenik, binakakosailkapenaren zenbait estrategi berritzaile proposatzen dira. Ekarpenhauek aldizkari edo konferentzi internazionaletan publikatuak edo bidaliakizan dira.Buruturiko experimentuetan, proposatutako algoritmoak artearen-estatuanaurkituriko zenbait algoritmorekin konparatu dira, emaitza interesgarriak lortuaz.Honetaz gain, emaitza hauetatik ondorio esanguratsuak eskuratzeko asmoz,test estatistikoen erabilera ere burutu da