213 research outputs found
Back propagation with balanced MSE cost Function and nearest neighbor editing for handling class overlap and class imbalance
The class imbalance problem has been considered a critical factor for designing and constructing the supervised classifiers. In the case of artificial neural networks, this complexity negatively affects the generalization process on under-represented classes. However, it has also been observed that the decrease in the performance attainable of standard learners is not directly caused by the class imbalance, but is also related with other difficulties, such as overlapping. In this work, a new empirical study for handling class overlap and class imbalance on multi-class problem is described. In order to solve this problem, we propose the joint use of editing techniques and a modified MSE cost function for MLP. This analysis was made on a remote sensing data . The experimental results demonstrate the consistency and validity of the combined strategy here proposedPartially supported by the Spanish Ministry of Education and Science under grants CSD2007–00018, TIN2009–14205–C04–04, and by Fundació Caixa Castelló–Bancaixa under grants P1–1B2009–04 and P1–1B2009–45; SDMAIA-010 of the TESJO and 2933/2010 from the UAE
A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios
Class imbalance and class overlap are two of the major problems in data mining and machine learning. Several studies have shown that these data complexities may affect the performance or behavior of artificial neural networks. Strategies proposed to face with both challenges have been separately applied. In this paper, we introduce a hybrid method for handling both class imbalance and class overlap simultaneously in multi-class learning problems. Experimental results on five remote sensing data show that the combined approach is a promising method
Exploiting gan as an oversampling method for imbalanced data augmentation with application to the fault diagnosis of an industrial robot
O diagnóstico inteligente de falhas baseado em aprendizagem máquina geralmente requer
um conjunto de dados balanceados para produzir um desempenho aceitável. No
entanto, a obtenção de dados quando o equipamento industrial funciona com falhas é
uma tarefa desafiante, resultando frequentemente num desequilíbrio entre dados obtidos
em condições nominais e com falhas. As técnicas de aumento de dados são das
abordagens mais promissoras para mitigar este problema.
Redes adversárias generativas (GAN) são um tipo de modelo generativo que consiste
de um módulo gerador e de um discriminador. Por meio de aprendizagem adversária
entre estes módulos, o gerador otimizado pode produzir padrões sintéticos que
podem ser usados para amumento de dados.
Investigamos se asGANpodem ser usadas como uma ferramenta de sobre amostra-
-gem para compensar um conjunto de dados desequilibrado em uma tarefa de diagnóstico
de falhas num manipulador robótico industrial. Realizaram-se uma série de
experiências para validar a viabilidade desta abordagem. A abordagem é comparada
com seis cenários, incluindo o método clássico de sobre amostragem SMOTE. Os resultados
mostram que a GAN supera todos os cenários comparados.
Para mitigar dois problemas reconhecidos no treino das GAN, ou seja, instabilidade
de treino e colapso de modo, é proposto o seguinte.
Propomos uma generalização da GAN de erro quadrado médio (MSE GAN) da
Wasserstein GAN com penalidade de gradiente (WGAN-GP), referida como VGAN (GAN baseado numa matriz V) para mitigar a instabilidade de treino. Além disso,
propomos um novo critério para rastrear o modelo mais adequado durante o treino.
Experiências com o MNIST e no conjunto de dados do manipulador robótico industrial
mostram que o VGAN proposto supera outros modelos competitivos.
A rede adversária generativa com consistência de ciclo (CycleGAN) visa lidar com
o colapso de modo, uma condição em que o gerador produz pouca ou nenhuma variabilidade.
Investigamos a distância fatiada de Wasserstein (SWD) na CycleGAN. O
SWD é avaliado tanto no CycleGAN incondicional quanto no CycleGAN condicional
com e sem mecanismos de compressão e excitação. Mais uma vez, dois conjuntos de
dados são avaliados, ou seja, o MNIST e o conjunto de dados do manipulador robótico
industrial. Os resultados mostram que o SWD tem menor custo computacional e supera
o CycleGAN convencional.Machine learning based intelligent fault diagnosis often requires a balanced data set for
yielding an acceptable performance. However, obtaining faulty data from industrial
equipment is challenging, often resulting in an imbalance between data acquired in
normal conditions and data acquired in the presence of faults. Data augmentation
techniques are among the most promising approaches to mitigate such issue.
Generative adversarial networks (GAN) are a type of generative model consisting
of a generator module and a discriminator. Through adversarial learning between
these modules, the optimised generator can produce synthetic patterns that can be
used for data augmentation.
We investigate whether GAN can be used as an oversampling tool to compensate
for an imbalanced data set in an industrial robot fault diagnosis task. A series of experiments
are performed to validate the feasibility of this approach. The approach is
compared with six scenarios, including the classical oversampling method (SMOTE).
Results show that GAN outperforms all the compared scenarios.
To mitigate two recognised issues in GAN training, i.e., instability and mode collapse,
the following is proposed.
We proposed a generalization of both mean sqaure error (MSE GAN) and Wasserstein
GAN with gradient penalty (WGAN-GP), referred to as VGAN (the V-matrix
based GAN) to mitigate training instability. Also, a novel criterion is proposed to keep
track of the most suitable model during training. Experiments on both the MNIST and the industrial robot data set show that the proposed VGAN outperforms other
competitive models.
Cycle consistency generative adversarial network (CycleGAN) is aiming at dealing
with mode collapse, a condition where the generator yields little to none variability.
We investigate the sliced Wasserstein distance (SWD) for CycleGAN. SWD is evaluated
in both the unconditional CycleGAN and the conditional CycleGAN with and
without squeeze-and-excitation mechanisms. Again, two data sets are evaluated, i.e.,
the MNIST and the industrial robot data set. Results show that SWD has less computational
cost and outperforms conventional CycleGAN
Machine Learning Methodologies for Interpretable Compound Activity Predictions
Machine learning (ML) models have gained attention for mining the pharmaceutical data that are currently generated at unprecedented rates and potentially accelerate the discovery of new drugs. The advent of deep learning (DL) has also raised expectations in pharmaceutical research. A central task in drug discovery is the initial search of compounds with desired biological activity. ML algorithms are able to find patterns in compound structures that are related to bioactivity, the so-called structure-activity relationships (SARs). ML-based predictions can complement biological testing to prioritize further experiments. Moreover, insights into model decisions are highly desired for further validation and identification of activity-relevant substructures. However, the interpretation of complex ML models remains essentially prohibitive. This thesis focuses on ML-based predictions of compound activity against multiple biological targets. Single-target and multi-target models are generated for relevant tasks including the prediction of profiling matrices from screening data and the discrimination between weak and strong inhibitors for more than a hundred kinases. Moreover, the relative performance of distinct modeling strategies is systematically analyzed under varying training conditions, and practical guidelines are reported. Since explainable model decisions are a clear requirement for the utility of ML bioactivity models in pharmaceutical research, methods for the interpretation and intuitive visualization of activity predictions from any ML or DL model are introduced. Taken together, this dissertation presents contributions that advance in the application and rationalization of ML models for biological activity and SAR predictions
Advances in Artificial Intelligence: Models, Optimization, and Machine Learning
The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications
Machine Learning in Tribology
Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology
Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming
Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management
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