Improving adaptive bagging methods for evolving data streams

We propose two new improvements for bagging methods on evolving data streams. Recently, two new variants of Bagging were proposed: ADWIN Bagging and Adaptive-Size Hoeffding Tree (ASHT) Bagging. ASHT Bagging uses trees of different sizes, and ADWIN Bagging uses ADWIN as a change detector to decide when to discard underperforming ensemble members. We improve ADWIN Bagging using Hoeffding Adaptive Trees, trees that can adaptively learn from data streams that change over time. To speed up the time for adapting to change of Adaptive-Size Hoeffding Tree (ASHT) Bagging, we add an error change detector for each classifier. We test our improvements by performing an evaluation study on synthetic and real-world datasets comprising up to ten million examples

Rotor Unbalance Kind and Severity Identification by Current Signature Analysis with Adaptative Update to Multiclass Machine Learning Algorithms

The health of a rotating electric machine can be evaluated by monitoring electrical and mechanical parameters. As more information is available, it easier can become the diagnosis of the machine operational condition. We built a laboratory test bench to study rotor unbalance issues according to ISO standards. Using the electric stator current harmonic analysis, this paper presents a comparison study among Support-Vector Machines, Decision Tree classifies, and One-vs-One strategy to identify rotor unbalance kind and severity problem – a nonlinear multiclass task. Moreover, we propose a methodology to update the classifier for dealing better with changes produced by environmental variations and natural machinery usage. The adaptative update means to update the training data set with an amount of recent data, saving the entire original historical data. It is relevant for engineering maintenance. Our results show that the current signature analysis is appropriate to identify the type and severity of the rotor unbalance problem. Moreover, we show that machine learning techniques can be effective for an industrial application

Efeito protetor ("savener") de inseticidas contra a fitotoxicidade causada pelo herbicida clomazone no algodoeiro.

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Temporal–spectral signaling of sensory information and expectations in the cerebral processing of pain

The perception of pain is shaped by somatosensory information about threat. However, pain is also influenced by an individual's expectations. Such expectations can result in clinically relevant modulations and abnormalities of pain. In the brain, sensory information, expectations (predictions), and discrepancies thereof (prediction errors) are signaled by an extended network of brain areas which generate evoked potentials and oscillatory responses at different latencies and frequencies. However, a comprehensive picture of how evoked and oscillatory brain responses signal sensory information, predictions, and prediction errors in the processing of pain is lacking so far. Here, we therefore applied brief painful stimuli to 48 healthy human participants and independently modulated sensory information (stimulus intensity) and expectations of pain intensity while measuring brain activity using electroencephalography (EEG). Pain ratings confirmed that pain intensity was shaped by both sensory information and expectations. In contrast, Bayesian analyses revealed that stimulus-induced EEG responses at different latencies (the N1, N2, and P2 components) and frequencies (alpha, beta, and gamma oscillations) were shaped by sensory information but not by expectations. Expectations, however, shaped alpha and beta oscillations before the painful stimuli. These findings indicate that commonly analyzed EEG responses to painful stimuli are more involved in signaling sensory information than in signaling expectations or mismatches of sensory information and expectations. Moreover, they indicate that the effects of expectations on pain are served by brain mechanisms which differ from those conveying effects of sensory information on pain

Avaliação dos impactos da pesquisa da Embrapa: uma amostra de 12 tecnologias.

bitstream/item/124521/1/DOC-13-SGE-1.pdfAutores: Ademir Francisco Girotto, Adriana Marlene Moreno Pires, Adriano Lincoln Albuquerque Mattos, Airton Manzano, Alcido Elenor Wander, Anna Cristina Lanna, Antonio Flavio Dias Ávila, Antonio Souza do Nascimento, Artur Chinelato de Camargo, Bonifácio Hideyuki Nakasu, Carlos Estevão Leite Cardoso, Carlos Magri Ferreira, Cladenor Pinho de Sá, Clayton Campanhola, Clóvis Oliveira de Almeida, Elsio Antônio Pereira de Figueiredo, Francisco Carlos da Rocha Gomes, Francisco Fábio de Assis Paiva, Geraldo Stachetti Rodrigues, Graciela Luzia Vedovoto, Honorino Roque Rodigheri, Jarbas Yukio Shimizu, João Carlos Medeiros Madail, José Alexandre Freitas Barrigossi, José Francisco Martins Pereira, José Lincoln Pinheiro Araújo, Júlio César Palhares, Loiva Maria Ribeiro de Mello, Luciano Gebler, Luiz Clovis Belarmino, Luiz José Maria Irias, Maria Cléa Brito de Figueiredo, Maria do Carmo Bassols Raseira, Marília Castelo Magalhães, Morsyleide de Freitas Rosa, Nelson Nogueira Barros, Nirlene Junqueira Vilela, Odo Primavesi, Oscar Tupy, Paulo Choji Kitamura, Pedro Felizardo Adeodato de Paula Pessoa, Péricles de Carvalho F. Neves, Rodrigo Siedler de Melo, Roberto Alonso Silveira