1,806 research outputs found
Evolving Ensemble Fuzzy Classifier
The concept of ensemble learning offers a promising avenue in learning from
data streams under complex environments because it addresses the bias and
variance dilemma better than its single model counterpart and features a
reconfigurable structure, which is well suited to the given context. While
various extensions of ensemble learning for mining non-stationary data streams
can be found in the literature, most of them are crafted under a static base
classifier and revisits preceding samples in the sliding window for a
retraining step. This feature causes computationally prohibitive complexity and
is not flexible enough to cope with rapidly changing environments. Their
complexities are often demanding because it involves a large collection of
offline classifiers due to the absence of structural complexities reduction
mechanisms and lack of an online feature selection mechanism. A novel evolving
ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in
this paper. pENsemble differs from existing architectures in the fact that it
is built upon an evolving classifier from data streams, termed Parsimonious
Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism,
which estimates a localized generalization error of a base classifier. A
dynamic online feature selection scenario is integrated into the pENsemble.
This method allows for dynamic selection and deselection of input features on
the fly. pENsemble adopts a dynamic ensemble structure to output a final
classification decision where it features a novel drift detection scenario to
grow the ensemble structure. The efficacy of the pENsemble has been numerically
demonstrated through rigorous numerical studies with dynamic and evolving data
streams where it delivers the most encouraging performance in attaining a
tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams
Existing FNNs are mostly developed under a shallow network configuration
having lower generalization power than those of deep structures. This paper
proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be
automatically extracted from data streams or removed if they play limited role
during their lifespan. The structure of the network can be deepened on demand
by stacking additional layers using a drift detection method which not only
detects the covariate drift, variations of input space, but also accurately
identifies the real drift, dynamic changes of both feature space and target
space. DEVFNN is developed under the stacked generalization principle via the
feature augmentation concept where a recently developed algorithm, namely
gClass, drives the hidden layer. It is equipped by an automatic feature
selection method which controls activation and deactivation of input attributes
to induce varying subsets of input features. A deep network simplification
procedure is put forward using the concept of hidden layer merging to prevent
uncontrollable growth of dimensionality of input space due to the nature of
feature augmentation approach in building a deep network structure. DEVFNN
works in the sample-wise fashion and is compatible for data stream
applications. The efficacy of DEVFNN has been thoroughly evaluated using seven
datasets with non-stationary properties under the prequential test-then-train
protocol. It has been compared with four popular continual learning algorithms
and its shallow counterpart where DEVFNN demonstrates improvement of
classification accuracy. Moreover, it is also shown that the concept drift
detection method is an effective tool to control the depth of network structure
while the hidden layer merging scenario is capable of simplifying the network
complexity of a deep network with negligible compromise of generalization
performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Accurate diagnosis of tool wear in metal turning process remains an open
challenge for both scientists and industrial practitioners because of
inhomogeneities in workpiece material, nonstationary machining settings to suit
production requirements, and nonlinear relations between measured variables and
tool wear. Common methodologies for tool condition monitoring still rely on
batch approaches which cannot cope with a fast sampling rate of metal cutting
process. Furthermore they require a retraining process to be completed from
scratch when dealing with a new set of machining parameters. This paper
presents an online tool condition monitoring approach based on Parsimonious
Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly
flexible principle where both ensemble structure and base-classifier structure
can automatically grow and shrink on the fly based on the characteristics of
data streams. Moreover, the online feature selection scenario is integrated to
actively sample relevant input attributes. The paper presents advancement of a
newly developed ensemble learning algorithm, pENsemble+, where online active
learning scenario is incorporated to reduce operator labelling effort. The
ensemble merging scenario is proposed which allows reduction of ensemble
complexity while retaining its diversity. Experimental studies utilising
real-world manufacturing data streams and comparisons with well known
algorithms were carried out. Furthermore, the efficacy of pENsemble was
examined using benchmark concept drift data streams. It has been found that
pENsemble+ incurs low structural complexity and results in a significant
reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic
Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments
The feasibility of deep neural networks (DNNs) to address data stream
problems still requires intensive study because of the static and offline
nature of conventional deep learning approaches. A deep continual learning
algorithm, namely autonomous deep learning (ADL), is proposed in this paper.
Unlike traditional deep learning methods, ADL features a flexible structure
where its network structure can be constructed from scratch with the absence of
an initial network structure via the self-constructing network structure. ADL
specifically addresses catastrophic forgetting by having a different-depth
structure which is capable of achieving a trade-off between plasticity and
stability. Network significance (NS) formula is proposed to drive the hidden
nodes growing and pruning mechanism. Drift detection scenario (DDS) is put
forward to signal distributional changes in data streams which induce the
creation of a new hidden layer. The maximum information compression index
(MICI) method plays an important role as a complexity reduction module
eliminating redundant layers. The efficacy of ADL is numerically validated
under the prequential test-then-train procedure in lifelong environments using
nine popular data stream problems. The numerical results demonstrate that ADL
consistently outperforms recent continual learning methods while characterizing
the automatic construction of network structures
Evolving clustering, classification and regression with TEDA
In this article the novel clustering and regression methods TEDACluster and TEDAPredict methods are described additionally to recently proposed evolving classifier TEDAClass. The algorithms for classification, clustering and regression are based on the recently proposed AnYa type fuzzy rule based system. The novel methods use the recently proposed TEDA framework capable of recursive processing of large amounts of data. The framework is capable of computationally cheap exact update of data per sample, and can be used for training `from scratch'. All three algorithms are evolving that is they are capable of changing its own structure during the update stage, which allows to follow the changes within the model pattern
Unsupervised classification of data streams based on typicality and eccentricity data analytics
In this paper, we propose a novel approach to unsupervised and online data classification. The algorithm is based on the statistical analysis of selected features and development of a self-evolving fuzzy-rule-basis. It starts learning from an empty rule basis and, instead of offline training, it learns “on-the-fly”. It is free of parameters and, thus, fuzzy rules, number, size or radius of the classes do not need to be pre-defined. It is very suitable for the classification of online data streams with realtime constraints. The past data do not need to be stored in memory, since that the algorithm is recursive, which makes it memory and computational power efficient. It is able to handle concept-drift and concept-evolution due to its evolving nature, which means that, not only rules/classes can be updated, but new classes can be created as new concepts emerge from the data. It can perform fuzzy classification/soft-labeling, which is preferred over traditional crisp classification in many areas of application. The algorithm was validated with an industrial pilot plant, where online calculated period and amplitude of control signal were used as input to a fault diagnosis application. The approach, however, is generic and can be applied to different problems and with much higher dimensional inputs. The results obtained from the real data are very significant
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Artificial Immune Systems - Models, algorithms and applications
Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun
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