Automated multi-label text categorization with VG-RAM weightless neural networks

Souza, Alberto F. De; Pedroni, Felipe; Oliveira, Elias; Ciarelli, Patrick Marques; Henrique, Wallace Favoreto; Veronese, Lucas; Badué, Claudine

doi:10.1016/j.neucom.2008.06.028

Cited by 44 publications

(23 citation statements)

References 17 publications

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“…In a previous work [6], we compared the VG-RAM WNN performance with that of the multi-label lazy learning technique (ML-KNN) proposed by Zhang and Zhou [18]. Their technique achieved higher performance than many well-established algorithms in several multi-label problems [18]; however, our experiments showed that VG-RAM WNN outperforms ML-KNN in a number of multi-label text categorization metrics.…”

Section: Introductionmentioning

confidence: 82%

Improving VG-RAM WNN Multi-label Text Categorization via Label Correlation

Souza

Badué

Melotti

et al. 2008

2008 Eighth International Conference on Intelligent Systems Design and Applications

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Section: Introductionmentioning

confidence: 82%

Improving VG-RAM WNN Multi-label Text Categorization via Label Correlation

Souza

Badué

Melotti

et al. 2008

2008 Eighth International Conference on Intelligent Systems Design and Applications

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“…However, there is no generalization property in the RAM neurons. There are several extensions of RAM neurons in which they try to smooth the non-generalization problems of RAM neurons, such as PLN, GSN, and G-RAM [21].…”

Section: Ram-based Neural Network Architecturementioning

confidence: 99%

Simple Pyramid RAM-Based Neural Network Architecture for Localization of Swarm Robots

2015

J Inf Process Syst

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The localization of multi-agents, such as people, animals, or robots, is a requirement to accomplish several tasks. Especially in the case of multi-robotic applications, localization is the process for determining the positions of robots and targets in an unknown environment. Many sensors like GPS, lasers, and cameras are utilized in the localization process. However, these sensors produce a large amount of computational resources to process complex algorithms, because the process requires environmental mapping. Currently, combination multi-robots or swarm robots and sensor networks, as mobile sensor nodes have been widely available in indoor and outdoor environments. They allow for a type of efficient global localization that demands a relatively low amount of computational resources and for the independence of specific environmental features. However, the inherent instability in the wireless signal does not allow for it to be directly used for very accurate position estimations and making difficulty associated with conducting the localization processes of swarm robotics system. Furthermore, these swarm systems are usually highly decentralized, which makes it hard to synthesize and access global maps, it can be decrease its flexibility. In this paper, a simple pyramid RAM-based Neural Network architecture is proposed to improve the localization process of mobile sensor nodes in indoor environments. Our approach uses the capabilities of learning and generalization to reduce the effect of incorrect information and increases the accuracy of the agent's position. The results show that by using simple pyramid RAM-base Neural Network approach, produces low computational resources, a fast response for processing every changing in environmental situation and mobile sensor nodes have the ability to finish several tasks especially in localization processes in real time.

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“…Because of that, we consider the fault diagnosis problem as a multi-label classification task [9]. Each fault category is represented by a binary predictor, diagnosing the presence or absence of that fault in an input pattern; the global classification system combines the fault specific decisions in order to completely diagnose a pattern.…”

Section: The Problem Domainmentioning

confidence: 99%

A Comparison of Two Feature-Based Ensemble Methods for Constructing Motor Pump Fault Diagnosis Classifiers

Oliveira

Wandekokem

Mendel

et al. 2010

2010 22nd IEEE International Conference on Tools With Artificial Intelligence

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This paper presents the results achieved by fault classifier ensembles based on a model-free supervised learning approach for diagnosing faults on oil rigs motor pumps. The main goal is to compare two feature-based ensemble construction methods, and present a third variation from one of them. The use of ensembles instead of single classifier systems has been widely applied in classification problems lately. The diversification of classifiers performed by the methods presented in this work is obtained by varying the feature set each classifier uses, and also at one point, alternating the intrinsic parameters for the training algorithm. We show results obtained with the established genetic algorithm GEFS and our recently developed approach called BSFS, which has a lower computational cost. We rely on a database of real data, with 2000 acquisitions of vibration signals extracted from operational motor pumps. Our results compare the outcomes from the two methods mentioned, and present a modification in one of them that improved the accuracy, reinforcing the motivation for the usage of that method.

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Automated multi-label text categorization with VG-RAM weightless neural networks

Cited by 44 publications

References 17 publications

Improving VG-RAM WNN Multi-label Text Categorization via Label Correlation

Improving VG-RAM WNN Multi-label Text Categorization via Label Correlation

Simple Pyramid RAM-Based Neural Network Architecture for Localization of Swarm Robots

A Comparison of Two Feature-Based Ensemble Methods for Constructing Motor Pump Fault Diagnosis Classifiers

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