Walmir M. Caminhas

Review: A review of machine learning approaches to Spam filtering

In this paper, we present a comprehensive review of recent developments in the application of machine learning algorithms to Spam filtering, focusing on both textual- and image-based approaches. Instead of considering Spam filtering as a standard classification problem, we highlight the importance of considering specific characteristics of the problem, especially concept drift, in designing new filters. Two particularly important aspects not widely recognized in the literature are discussed: the difficulties in updating a classifier based on the bag-of-words representation and a major difference between two early naive Bayes models. Overall, we conclude that while important advancements have been made in the last years, several aspects remain to be explored, especially under more realistic evaluation settings.

Multivariable Gaussian Evolving Fuzzy Modeling System

This paper introduces a class of evolving fuzzy rule-based system as an approach for multivariable Gaussian adaptive fuzzy modeling. The system is an evolving Takagi-Sugeno (eTS) functional fuzzy model, whose rule base can be continuously updated using a new recursive clustering algorithm based on participatory learning. The fuzzy sets of the rule antecedents are multivariable Gaussian membership functions, which have been adopted to preserve information between input variable interactions. The parameters of the membership functions are estimated by the clustering algorithm. A weighted recursive least-squares algorithm updates the parameters of the rule consequents. Experiments considering time-series forecasting and nonlinear system identification are performed to evaluate the performance of the approach proposed. The multivariable Gaussian evolving fuzzy models are compared with alternative evolving fuzzy models and classic models with fixed structures. The results suggest that multivariable Gaussian evolving fuzzy modeling is a promising approach for adaptive system modeling.

Adaptive fault detection and diagnosis using an evolving fuzzy classifier

This paper suggests an approach for adaptive fault detection and diagnosis. The proposed approach detects new operation modes of a process such as operation point changes and faults, and incorporates information about operation modes in an evolving fuzzy classifier used for diagnosis. The approach relies upon an incremental clustering procedure to generate fuzzy rules describing new operational states detected. The classifier performs diagnostic adaptively and, since every new operation mode detected is learnt and incorporated into the classifier, it is capable of identifying the same operation mode the next time it occurs. The efficiency of the approach is verified in fault detection and diagnosis of an industrial actuator. Experimental results suggest that the approach is a promising alternative for fault diagnosis of dynamic systems when there is no a priori information about all failure modes, and as an alternative to incremental learning of diagnosis systems using data streams.

Intelligent Thermographic Diagnostic Applied to Surge Arresters: A New Approach

This paper describes a methodology that aims to extract information to enable the detection and diagnosis of faults in surge arresters, using the thermovision technique. Thermovision is a non-destructive technique used in diverse services of maintenance, having the advantage not to demand the disconnection of the equipment. The methodology uses a digital image processing algorithm based on the Watershed Transform to get the segmentation of the surge arresters. By applying the methodology is possible to classify surge arresters operative condition in: faulty, normal, light, and suspicious. The computational system generated train its neuro-fuzzy network by using a historical thermovision data. During the train phase, a heuristic is proposed in order to set the number of networks in the diagnosis system. This system was validated by a database with a hundreds of different faulty sceneries. The validation error of the set of neuro-fuzzy and the automatic digital thermovision image processing was about 10%t. The diagnosis system described has been successfully used by Electric Energy Research Center as a decision making tool for surge arresters fault diagnosis.

Design of an Artificial Immune System for fault detection: A Negative Selection Approach

This paper presents a methodology that designs a fault detection Artificial Immune System (AIS) based on immune theory. The fault detection is a challenging problem due to increasing complexity of processes and agility necessary to avoid malfunction or accidents. The key fault detection challenge is determining the difference between normal and potential harmful activities. A promising solution is emerging in the form of AIS. The SelfxNonself theory inspired an immune-based fault detection approach. This article proposes the AIS Multi-Operational Algorithm based on the Negative Selection Algorithm. The proposed algorithm is used to a DC motor fault model benchmark to compare its relative performance to others fault detection algorithms. The results show that the strategy developed is promising for incipient and abrupt fault detection.

A fast learning algorithm for evolving neo-fuzzy neuron

This paper introduces an evolving neural fuzzy modeling approach constructed upon the neo-fuzzy neuron and network. The approach uses an incremental learning scheme to simultaneously granulate the input space and update the neural network weights. The neural network structure and parameters evolve simultaneously as data are input. Initially the space of each input variable is granulated using two complementary triangular membership functions. New triangular membership functions may be added, excluded and/or have their parameters adjusted depending on the input data and modeling error. The parameters of the network are updated using a gradient-based scheme with optimal learning rate. The performance of the approach is evaluated using instances of times series forecasting and nonlinear system identification problems. Computational experiments and comparisons against alternative evolving models show that the evolving neural neo-fuzzy network is accurate and fast, characteristics which are essential for adaptive systems modeling, especially in real-time, on-line environments.

Fuzzy evolving linear regression trees

This paper introduces a new approach for evolving fuzzy modeling using tree structures. The model is a fuzzy linear regression tree whose topology can be continuously updated through a statistical model selection test. A fuzzy linear regression tree is a fuzzy tree with linear model in each leaf. An incremental learning algorithm approach evolves the tree replacing leaves with subtrees that improve the model quality. The learning algorithm evaluates each input only once and do not need to store any past values. The evolving linear regression model is evaluated using time series forecasting problems. The performance is compared against alternative evolving fuzzy models and classic models with fixed structures. The results suggest that fuzzy evolving regression tree is a promising approach for adaptive system modeling.

Incipient fault detection in induction machine stator-winding using a fuzzy-Bayesian change point detection approach

In this paper the incipient fault detection problem in induction machine stator-winding is considered. The problem is solved using a new technique of change point detection in time series, based on a three-step formulation. The technique can detect up to two change points in the time series. The first step consists of a Kohonen neural network classification algorithm that defines the model to be used, one change point or two change points. The second step consists of a fuzzy clustering to transform the initial data, with arbitrary distribution, into a new one that can be approximated by a beta distribution. The fuzzy cluster centers are determined by using the Kohonen neural network classification algorithm used in the first step. The last step consists in using the Metropolis-Hastings algorithm for performing the change point detection in the transformed time series generated by the second step with that known distribution. The incipient faults are detected as long as they characterize change points in such transformed time series. The main contribution of the proposed approach in this paper, related to previous one in the Literature, is to detect up to two change points in the time series considered, besides the enhanced resilience of the new fault detection procedure against false alarms, combined with a good sensitivity that allows the detection of rather small fault signals. Simulation results are presented to illustrate the proposed methodology.

A novel Artificial Immune System for fault behavior detection

Research highlights? Fault detection problem is addressed via the Natural Killer immune cells mechanisms. ? The Natural Killer immune cells mechanisms inspired an Artificial Immune System. ? The difference between normal and harmful activities generates an alarm scheme. ? The DAMADICS benchmark is used to compare the proposed methodology to other ones. This paper presents an error detection methodology to enable fault detection inspired on recent immune theory. The fault detection problem is a challenging problem due to processes increasing complexity and agility necessary to avoid malfunction or accidents. The key challenge is determining the difference between normal and potential harmful activities. A promising solution is emerging in the form of Artificial Immune System (AIS). In this article, Natural Killer (NK) immune cells mechanisms inspired an AIS. The AIS proposed uses recent biological mechanism such as: NK activation and education machinery. DAMADICS benchmark was applied to compare the proposed AIS performance to others fault detection algorithms. The results show that the novel approach developed provides better detection rate and false alarms tradeoff when compared to other methods in literature.

Design of an artificial immune system based on Danger Model for fault detection

This paper presents a methodology that enables fault detection in dynamic systems based on recent immune theory. The fault detection is a challenging problem due to increasing complexity of processes and agility necessary to avoid malfunction or accidents. The fault detection central challenge is determining the difference between normal and potential harmful activities at dynamic systems. A promising solution is emerging in the form of Artificial Immune Systems (AIS). The Danger Model (DM) proposes that the immune system reacts not against self or non-self but by threats generated into the organism: the danger signals. DM-based fault detection system proposes a new formulation for a fault detection system. A DM-inspired methodology is applied to a fault detection benchmark provided by DAMADICS to compare its relative performance to others algorithms. The results show that the strategy developed is promising for incipient and abrupt fault detection in dynamic systems.