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Roberto Souto Maior De Barros - One of the best experts on this subject based on the ideXlab platform.
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Concept Drift detection based on Fishers Exact test
Information Sciences, 2018Co-Authors: Danilo Rafael De Lima Cabral, Roberto Souto Maior De BarrosAbstract:Efficient implementation of computationally expensive Fishers Exact Test.Three new Concept Drift detection methods based on Fishers Exact Test.Tested against DDM, ECDD, SEED, FHDDM, and STEPD using two base classifiers.Proposed methods are significantly superior to most other Detectors in accuracy.Proposed methods have better Precision, Recall and F-Measure than the other methods. Concept Drift detectors are software that usually attempt to estimate the positions of Concept Drifts in large data streams in order to replace the base learner after changes in the data distribution and thus improve accuracy. Statistical Test of Equal Proportions (STEPD) is a simple, efficient, and well-known method which detects Concept Drifts based on a hypothesis test between two proportions. However, statistically, this test is not recommended when sample sizes are small or data are sparse and/or imbalanced. This article proposes an ingeniously efficient implementation of the statistically preferred but computationally expensive Fishers Exact test and examines three slightly different applications of this test for Concept Drift detection, proposing FPDD, FSDD, and FTDD. Experiments run using four artificial dataset generators, with both abrupt and gradual Drift versions, as well as three real-world datasets, suggest that the new methods improve the accuracy results and the detections of STEPD and other well-known and/or recent Concept Drift detectors in many scenarios, with little impact on memory and run-time usage.
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A large-scale comparison of Concept Drift detectors
Information Sciences, 2018Co-Authors: Roberto Souto Maior De Barros, Silas Garrido Teixeira De Carvalho SantosAbstract:Abstract Online learning involves extracting information from large quantities of data (streams) usually affected by changes in the distribution (Concept Drift). A Drift detector is a small program that estimates the positions of these changes to replace the base learner and ultimately improve overall accuracy. This article reports on a large-scale comparison of 14 Concept Drift detector configurations for mining fully labeled data streams with Concept Drift, using a large number of artificial datasets and two different base classifiers (Naive Bayes and Hoeffding Tree). The goal is to adequately measure how good the existent Concept Drift detectors really are and also to verify and challenge a common belief in the area, namely that the best Drift detection methods are necessarily those that detect all the existing Drifts closer to their correct positions, and only them, irrespective of the fact that different objectives usually require alternative solutions. Finally, to some extent, this article may also be seen as an extensive literature survey of Concept Drift detectors.
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ICTAI - A Lightweight Concept Drift Detection Ensemble
2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), 2015Co-Authors: Bruno Iran Ferreira Maciel, Silas Garrido Teixeira De Carvalho Santos, Roberto Souto Maior De BarrosAbstract:Uncovering information from large data streams containing changes in the data distribution (Concept Drift) make online learning a challenge that is progressively becoming more relevant. This paper proposes Drift Detection Ensemble (DDE), a small ensemble classifier that aggregates the warnings and Drift detections of three Concept Drift detectors aiming to improve the results of the individual methods using different strategies and configurations. DDE was programmed to use different default combinations of detectors depending on the chosen sensitivity of the ensemble. Experiments were performed against six Drift detectors using their default configurations, comparing their results on multiple artificial datasets containing different frequencies and durations of Concept Drifts, as well as real-world datasets. Our results indicate that the best two methods were DDE versions and they were statistically superior to several detectors.
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RCD: A recurring Concept Drift framework
Pattern Recognition Letters, 2013Co-Authors: Paulo Gonçalves, Roberto Souto Maior De BarrosAbstract:This paper presents recurring Concept Drifts (RCD), a framework that offers an alternative approach to handle data streams that suffer from recurring Concept Drifts (on-line learning). It creates a new classifier to each context found and stores a sample of data used to build it. When a new Concept Drift occurs, the algorithm compares the new context to previous ones using a non-parametric multivariate statistical test to verify if both contexts come from the same distribution. If so, the corresponding classifier is reused. The RCD framework is compared with several algorithms (among single and ensemble approaches), in both artificial and real data sets, chosen from frequently used algorithms and data sets in the Concept Drift research area. We claim the proposed framework had better average ranks in data sets with abrupt and gradual Concept Drifts compared to both the single classifiers and the ensemble approaches that use the same base learner.
Manuel Roveri - One of the best experts on this subject based on the ideXlab platform.
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Learning Discrete-Time Markov Chains Under Concept Drift
IEEE transactions on neural networks and learning systems, 2019Co-Authors: Manuel RoveriAbstract:Learning under Concept Drift is a novel and promising research area aiming at designing learning algorithms able to deal with nonstationary data-generating processes. In this research field, most of the literature focuses on learning nonstationary probabilistic frameworks, while some extensions about learning graphs and signals under Concept Drift exist. For the first time in the literature, this paper addresses the problem of learning discrete-time Markov chains (DTMCs) under Concept Drift. More specifically, following a hybrid active/passive approach, this paper introduces both a family of change-detection mechanisms (CDMs), differing in the required assumptions and performance, for detecting changes in DTMCs and an adaptive learning algorithm able to deal with DTMCs under Concept Drift. The effectiveness of both the proposed CDMs and the adaptive learning algorithm has been extensively tested on synthetically generated experiments and real data sets.
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IJCNN - Learning Convolutional Neural Networks in presence of Concept Drift
2019 International Joint Conference on Neural Networks (IJCNN), 2019Co-Authors: Simone Disabato, Manuel RoveriAbstract:Designing adaptive machine learning systems able to operate in nonstationary conditions, also called Concept Drift, is a novel and promising research area. Convolutional Neural Networks (CNNs) have not been considered a viable solution for such adaptive systems due to the high computational load and the high number of images they require for the training. This paper introduces an adaptive mechanism for learning CNNs able to operate in presence of Concept Drift. Such an adaptive mechanism follows an "active approach", where the adaptation is triggered by the detection of a Concept Drift, and relies on the "transfer learning" paradigm to transfer (part of the) knowledge from the CNN operating before the Concept Drift to the one operating after. The effectiveness of the proposed solution has been evaluated on two types of CNNs and two real-world image benchmarks.
Hosik Choi - One of the best experts on this subject based on the ideXlab platform.
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Tracking Concept Drift using a constrained penalized regression combiner
Computational Statistics & Data Analysis, 2017Co-Authors: Li-yu Wang, Kyupil Yeon, Cheolwoo Park, Hosik ChoiAbstract:The objective of this work is to develop a predictive model when data batches are collected in a sequential manner. With streaming data, information is constantly being updated and a major statistical challenge for these types of data is that the underlying distribution and the true inputoutput dependency might change over time, a phenomenon known as Concept Drift. The Concept Drift phenomenon makes the learning process complicated because a predictive model constructed on the past data is no longer consistent with new examples. In order to effectively track Concept Drift, we propose model-combining methods using constrained and penalized regression that possesses a grouping property. The new learning methods enable us to select data batches as a group that are relevant to the current one, reduce the effects of irrelevant batches, and adaptively reflect the degree of Concept Drift emerging in data streams. We demonstrate the finite sample performance of the proposed method using simulated and real examples. The analytical and empirical results indicate that the proposed methods can effectively adapt to various types of Concept Drift.
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Model Averaging via Penalized Regression for Tracking Concept Drift
Journal of Computational and Graphical Statistics, 2010Co-Authors: Kyupil Yeon, Moon Sup Song, Yongdai Kim, Hosik Choi, Cheolwoo ParkAbstract:A supervised learning algorithm aims to build a prediction model using training examples. This paradigm typically has the assumptions that the underl ying distribution and the true input–output dependency do not change. However, these assumptions often fail to hold, especially in data streams. This phenomenon is known as Concept Drift. We propose a new model combining algorithm for tracking Concept Drift in data streams. The final predictive ensemble model has a form of a weighted average and ridge regression combiner. The coefficients of the combiner are determined by ridge regression with the constraints such that the coefficients are nonnegative and sum to 1. The proposed algorithm is devised via a new measure of Concept Drift, the angle between the estimated weights from data and the optimal weight vector obtained under no Concept Drift. It is shown that the ridge tuning parameter plays a crucial role of forcing the proposed algorithm to adapt to Concept Drift. Our main findings include (i) the propos...
Indrė žliobaitė - One of the best experts on this subject based on the ideXlab platform.
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an overview of Concept Drift applications
2016Co-Authors: Mykola Pechenizkiy, Indrė žliobaitė, Joao GamaAbstract:In most challenging data analysis applications, data evolve over time and must be analyzed in near real time. Patterns and relations in such data often evolve over time, thus, models built for analyzing such data quickly become obsolete over time. In machine learning and data mining this phenomenon is referred to as Concept Drift. The objective is to deploy models that would diagnose themselves and adapt to changing data over time. This chapter provides an application oriented view towards Concept Drift research, with a focus on supervised learning tasks. First we overview and categorize application tasks for which the problem of Concept Drift is particularly relevant. Then we construct a reference framework for positioning application tasks within a spectrum of problems related to Concept Drift. Finally, we discuss some promising research directions from the application perspective, and present recommendations for application driven Concept Drift research and development.
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a survey on Concept Drift adaptation
ACM Computing Surveys, 2014Co-Authors: Joao Gama, Albert Bifet, Indrė žliobaitė, Mykola Pechenizkiy, Abdelhamid BouchachiaAbstract:Concept Drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this article, we characterize adaptive learning processes; categorize existing strategies for handling Concept Drift; overview the most representative, distinct, and popular techniques and algorithms; discuss evaluation methodology of adaptive algorithms; and present a set of illustrative applications. The survey covers the different facets of Concept Drift in an integrated way to reflect on the existing scattered state of the art. Thus, it aims at providing a comprehensive introduction to the Concept Drift adaptation for researchers, industry analysts, and practitioners.
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Learning under Concept Drift: an Overview
arXiv: Artificial Intelligence, 2010Co-Authors: Indrė žliobaitėAbstract:Concept Drift refers to a non stationary learning problem over time. The training and the application data often mismatch in real life problems. In this report we present a context of Concept Drift problem 1. We focus on the issues relevant to adaptive training set formation. We present the framework and terminology, and formulate a global picture of Concept Drift learners design. We start with formalizing the framework for the Concept Drifting data in Section 1. In Section 2 we discuss the adaptivity mechanisms of the Concept Drift learners. In Section 3 we overview the principle mechanisms of Concept Drift learners. In this chapter we give a general picture of the available algorithms and categorize them based on their properties. Section 5 discusses the related research fields and Section 5 groups and presents major Concept Drift applications. This report is intended to give a bird's view of Concept Drift research field, provide a context of the research and position it within broad spectrum of research fields and applications.
Chunfu Jia - One of the best experts on this subject based on the ideXlab platform.
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An Ensemble Learning System to Mitigate Malware Concept Drift Attacks (Short Paper)
Information Security Practice and Experience, 2017Co-Authors: Zhi Wang, JunNan Wang, Meiqi Tian, Chunfu JiaAbstract:Machine learning is widely used in malware detection systems as a core component. However, machine learning algorithm is based on the assumption that the underlying malware Concept is stable for training and testing. The assumption is vulnerable to well-crafted Concept Drift attacks, such as mimicry attacks, gradient descent attacks, poisoning attacks and so on. This paper proposes an ensemble learning system which combines vertical and horizontal correlation learning models. The significant diversity among vertical and horizontal correlation models increases the difficulty of Concept Drift attacks. And average p-value assessment is applied to fortify the system to be sensitive to hidden Concept Drift. The experiment results show that the hybrid system could actively recognize the Concept Drift among different Miuref variants.