4,292 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
A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework
Class imbalance poses new challenges when it comes to classifying data
streams. Many algorithms recently proposed in the literature tackle this
problem using a variety of data-level, algorithm-level, and ensemble
approaches. However, there is a lack of standardized and agreed-upon procedures
on how to evaluate these algorithms. This work presents a taxonomy of
algorithms for imbalanced data streams and proposes a standardized, exhaustive,
and informative experimental testbed to evaluate algorithms in a collection of
diverse and challenging imbalanced data stream scenarios. The experimental
study evaluates 24 state-of-the-art data streams algorithms on 515 imbalanced
data streams that combine static and dynamic class imbalance ratios,
instance-level difficulties, concept drift, real-world and semi-synthetic
datasets in binary and multi-class scenarios. This leads to the largest
experimental study conducted so far in the data stream mining domain. We
discuss the advantages and disadvantages of state-of-the-art classifiers in
each of these scenarios and we provide general recommendations to end-users for
selecting the best algorithms for imbalanced data streams. Additionally, we
formulate open challenges and future directions for this domain. Our
experimental testbed is fully reproducible and easy to extend with new methods.
This way we propose the first standardized approach to conducting experiments
in imbalanced data streams that can be used by other researchers to create
trustworthy and fair evaluation of newly proposed methods. Our experimental
framework can be downloaded from
https://github.com/canoalberto/imbalanced-streams
CONTINUAL LEARNING FOR MULTI-LABEL DRIFTING DATA STREAMS USING HOMOGENEOUS ENSEMBLE OF SELF-ADJUSTING NEAREST NEIGHBORS
Multi-label data streams are sequences of multi-label instances arriving over time to a multi-label classifier. The properties of the data stream may continuously change due to concept drift. Therefore, algorithms must adapt constantly to the new data distributions. In this paper we propose a novel ensemble method for multi-label drifting streams named Homogeneous Ensemble of Self-Adjusting Nearest Neighbors (HESAkNN). It leverages a self-adjusting kNN as a base classifier with the advantages of ensembles to adapt to concept drift in the multi-label environment. To promote diverse knowledge within the ensemble, each base classifier is given a unique subset of features and samples to train on. These samples are distributed to classifiers in a probabilistic manner that follows a Poisson distribution as in online bagging. Accompanying these mechanisms, a collection of ADWIN detectors monitor each classifier for the occurrence of a concept drift. Upon detection, the algorithm automatically trains additional classifiers in the background to attempt to capture new concepts. After a pre-determined number of instances, both active and background classifiers are compared and only the most accurate classifiers are selected to populate the new active ensemble. The experimental study compares the proposed approach with 30 other classifiers including problem transformation, algorithm adaptation, kNNs, and ensembles on 30 diverse multi-label datasets and 11 performance metrics. Results validated using non-parametric statistical analysis support the better performance of the heterogeneous ensemble and highlights the contribution of the feature and instance diversity in improving the performance of the ensemble
A reduced labeled samples (RLS) framework for classification of imbalanced concept-drifting streaming data.
Stream processing frameworks are designed to process the streaming data that arrives in time. An example of such data is stream of emails that a user receives every day. Most of the real world data streams are also imbalanced as is in the stream of emails, which contains few spam emails compared to a lot of legitimate emails. The classification of the imbalanced data stream is challenging due to the several reasons: First of all, data streams are huge and they can not be stored in the memory for one time processing. Second, if the data is imbalanced, the accuracy of the majority class mostly dominates the results. Third, data streams are changing over time, and that causes degradation in the model performance. Hence the model should get updated when such changes are detected. Finally, the true labels of the all samples are not available immediately after classification, and only a fraction of the data is possible to get labeled in real world applications. That is because the labeling is expensive and time consuming. In this thesis, a framework for modeling the streaming data when the classes of the data samples are imbalanced is proposed. This framework is called Reduced Labeled Samples (RLS). RLS is a chunk based learning framework that builds a model using partially labeled data stream, when the characteristics of the data change. In RLS, a fraction of the samples are labeled and are used in modeling, and the performance is not significantly different from that of the 100% labeling. RLS maintains an ensemble of classifiers to boost the performance. RLS uses the information from labeled data in a supervised fashion, and also is extended to use the information from unlabeled data in a semi supervised fashion. RLS addresses both binary and multi class partially labeled data stream and the results show the basis of RLS is effective even in the context of multi class classification problems. Overall, the RLS is shown to be an effective framework for processing imbalanced and partially labeled data streams
Discrimination and Class Imbalance Aware Online Naive Bayes
Fairness-aware mining of massive data streams is a growing and challenging
concern in the contemporary domain of machine learning. Many stream learning
algorithms are used to replace humans at critical decision-making points e.g.,
hiring staff, assessing credit risk, etc. This calls for handling massive
incoming information with minimum response delay while ensuring fair and high
quality decisions. Recent discrimination-aware learning methods are optimized
based on overall accuracy. However, the overall accuracy is biased in favor of
the majority class; therefore, state-of-the-art methods mainly diminish
discrimination by partially or completely ignoring the minority class. In this
context, we propose a novel adaptation of Na\"ive Bayes to mitigate
discrimination embedded in the streams while maintaining high predictive
performance for both the majority and minority classes. Our proposed algorithm
is simple, fast, and attains multi-objective optimization goals. To handle
class imbalance and concept drifts, a dynamic instance weighting module is
proposed, which gives more importance to recent instances and less importance
to obsolete instances based on their membership in minority or majority class.
We conducted experiments on a range of streaming and static datasets and
deduced that our proposed methodology outperforms existing state-of-the-art
fairness-aware methods in terms of both discrimination score and balanced
accuracy
SMOClust: Synthetic Minority Oversampling based on Stream Clustering for Evolving Data Streams
Many real-world data stream applications not only suffer from concept drift
but also class imbalance. Yet, very few existing studies investigated this
joint challenge. Data difficulty factors, which have been shown to be key
challenges in class imbalanced data streams, are not taken into account by
existing approaches when learning class imbalanced data streams. In this work,
we propose a drift adaptable oversampling strategy to synthesise minority class
examples based on stream clustering. The motivation is that stream clustering
methods continuously update themselves to reflect the characteristics of the
current underlying concept, including data difficulty factors. This nature can
potentially be used to compress past information without caching data in the
memory explicitly. Based on the compressed information, synthetic examples can
be created within the region that recently generated new minority class
examples. Experiments with artificial and real-world data streams show that the
proposed approach can handle concept drift involving different minority class
decomposition better than existing approaches, especially when the data stream
is severely class imbalanced and presenting high proportions of safe and
borderline minority class examples.Comment: 59 pages, 85 figure
Large-Scale Online Semantic Indexing of Biomedical Articles via an Ensemble of Multi-Label Classification Models
Background: In this paper we present the approaches and methods employed in
order to deal with a large scale multi-label semantic indexing task of
biomedical papers. This work was mainly implemented within the context of the
BioASQ challenge of 2014. Methods: The main contribution of this work is a
multi-label ensemble method that incorporates a McNemar statistical
significance test in order to validate the combination of the constituent
machine learning algorithms. Some secondary contributions include a study on
the temporal aspects of the BioASQ corpus (observations apply also to the
BioASQ's super-set, the PubMed articles collection) and the proper adaptation
of the algorithms used to deal with this challenging classification task.
Results: The ensemble method we developed is compared to other approaches in
experimental scenarios with subsets of the BioASQ corpus giving positive
results. During the BioASQ 2014 challenge we obtained the first place during
the first batch and the third in the two following batches. Our success in the
BioASQ challenge proved that a fully automated machine-learning approach, which
does not implement any heuristics and rule-based approaches, can be highly
competitive and outperform other approaches in similar challenging contexts
Continual learning from stationary and non-stationary data
Continual learning aims at developing models that are capable of working on constantly evolving problems over a long-time horizon. In such environments, we can distinguish three essential aspects of training and maintaining machine learning models - incorporating new knowledge, retaining it and reacting to changes. Each of them poses its own challenges, constituting a compound problem with multiple goals.
Remembering previously incorporated concepts is the main property of a model that is required when dealing with stationary distributions. In non-stationary environments, models should be capable of selectively forgetting outdated decision boundaries and adapting to new concepts. Finally, a significant difficulty can be found in combining these two abilities within a single learning algorithm, since, in such scenarios, we have to balance remembering and forgetting instead of focusing only on one aspect.
The presented dissertation addressed these problems in an exploratory way. Its main goal was to grasp the continual learning paradigm as a whole, analyze its different branches and tackle identified issues covering various aspects of learning from sequentially incoming data. By doing so, this work not only filled several gaps in the current continual learning research but also emphasized the complexity and diversity of challenges existing in this domain. Comprehensive experiments conducted for all of the presented contributions have demonstrated their effectiveness and substantiated the validity of the stated claims
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
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