Combining similarity in time and space for training set formation under concept drift

Concept drift is a challenge in supervised learning for sequential data. It describes a phenomenon when the data distributions change over time. In such a case accuracy of a classifier benefits from the selective sampling for training. We develop a method for training set selection, particularly relevant when the expected drift is gradual. Training set selection at each time step is based on the distance to the target instance. The distance function combines similarity in space and in time. The method determines an optimal training set size online at every time step using cross validation. It is a wrapper approach, it can be used plugging in different base classifiers. The proposed method shows the best accuracy in the peer group on the real and artificial drifting data. The method complexity is reasonable for the field applications

A concept drift-tolerant case-base editing technique

© 2015 Elsevier B.V. All rights reserved. The evolving nature and accumulating volume of real-world data inevitably give rise to the so-called "concept drift" issue, causing many deployed Case-Based Reasoning (CBR) systems to require additional maintenance procedures. In Case-base Maintenance (CBM), case-base editing strategies to revise the case-base have proven to be effective instance selection approaches for handling concept drift. Motivated by current issues related to CBR techniques in handling concept drift, we present a two-stage case-base editing technique. In Stage 1, we propose a Noise-Enhanced Fast Context Switch (NEFCS) algorithm, which targets the removal of noise in a dynamic environment, and in Stage 2, we develop an innovative Stepwise Redundancy Removal (SRR) algorithm, which reduces the size of the case-base by eliminating redundancies while preserving the case-base coverage. Experimental evaluations on several public real-world datasets show that our case-base editing technique significantly improves accuracy compared to other case-base editing approaches on concept drift tasks, while preserving its effectiveness on static tasks

Active Collaborative Ensemble Tracking

A discriminative ensemble tracker employs multiple classifiers, each of which casts a vote on all of the obtained samples. The votes are then aggregated in an attempt to localize the target object. Such method relies on collective competence and the diversity of the ensemble to approach the target/non-target classification task from different views. However, by updating all of the ensemble using a shared set of samples and their final labels, such diversity is lost or reduced to the diversity provided by the underlying features or internal classifiers' dynamics. Additionally, the classifiers do not exchange information with each other while striving to serve the collective goal, i.e., better classification. In this study, we propose an active collaborative information exchange scheme for ensemble tracking. This, not only orchestrates different classifier towards a common goal but also provides an intelligent update mechanism to keep the diversity of classifiers and to mitigate the shortcomings of one with the others. The data exchange is optimized with regard to an ensemble uncertainty utility function, and the ensemble is updated via co-training. The evaluations demonstrate promising results realized by the proposed algorithm for the real-world online tracking.Comment: AVSS 2017 Submissio

Fuzzy competence model drift detection for data-driven decision support systems

© 2017 Elsevier B.V. This paper focuses on concept drift in business intelligence and data-driven decision support systems (DSSs). The assumption of a fixed distribution in the data renders conventional static DSSs inaccurate and unable to make correct decisions when concept drift occurs. However, it is important to know when, how, and where concept drift occurs so a DSS can adjust its decision processing knowledge to adapt to an ever-changing environment at the appropriate time. This paper presents a data distribution-based concept drift detection method called fuzzy competence model drift detection (FCM-DD). By introducing fuzzy sets theory and replacing crisp boundaries with fuzzy ones, we have improved the competence model to provide a better, more refined empirical distribution of the data stream. FCM-DD requires no prior knowledge of the underlying distribution and provides statistical guarantee of the reliability of the detected drift, based on the theory of bootstrapping. A series of experiments show that our proposed FCM-DD method can detect drift more accurately, has good sensitivity, and is robust

Active fuzzy weighting ensemble for dealing with concept drift

© 2018, the Authors. The concept drift problem is a pervasive phenomenon in real-world data stream applications. It makes well-trained static learning models lose accuracy and become outdated as time goes by. The existence of different types of concept drift makes it more difficult for learning algorithms to track. This paper proposes a novel adaptive ensemble algorithm, the Active Fuzzy Weighting Ensemble, to handle data streams involving concept drift. During the processing of data instances in the data streams, our algorithm first identifies whether or not a drift occurs. Once a drift is confirmed, it uses data instances accumulated by the drift detection method to create a new base classifier. Then, it applies fuzzy instance weighting and a dynamic voting strategy to organize all the existing base classifiers to construct an ensemble learning model. Experimental evaluations on seven datasets show that our proposed algorithm can shorten the recovery time of accuracy drop when concept drift occurs, adapt to different types of concept drift, and obtain better performance with less computation costs than the other adaptive ensembles

Concept drift detection based on anomaly analysis

© Springer International Publishing Switzerland 2014. In online machine learning, the ability to adapt to new concept quickly is highly desired. In this paper, we propose a novel concept drift detection method, which is called Anomaly Analysis Drift Detection (AADD), to improve the performance of machine learning algorithms under non-stationary environment. The proposed AADD method is based on an anomaly analysis of learner’s accuracy associate with the similarity between learners’ training domain and test data. This method first identifies whether there are conflicts between current concept and new coming data. Then the learner will incrementally learn the non conflict data, which will not decrease the accuracy of the learner on previous trained data, for concept extension. Otherwise, a new learner will be created based on the new data. Experiments illustrate that this AADD method can detect new concept quickly and learn extensional drift incrementally

Explainable Adaptive Tree-based Model Selection for Time Series Forecasting

Tree-based models have been successfully applied to a wide variety of tasks, including time series forecasting. They are increasingly in demand and widely accepted because of their comparatively high level of interpretability. However, many of them suffer from the overfitting problem, which limits their application in real-world decision-making. This problem becomes even more severe in online-forecasting settings where time series observations are incrementally acquired, and the distributions from which they are drawn may keep changing over time. In this context, we propose a novel method for the online selection of tree-based models using the TreeSHAP explainability method in the task of time series forecasting. We start with an arbitrary set of different tree-based models. Then, we outline a performance-based ranking with a coherent design to make TreeSHAP able to specialize the tree-based forecasters across different regions in the input time series. In this framework, adequate model selection is performed online, adaptively following drift detection in the time series. In addition, explainability is supported on three levels, namely online input importance, model selection, and model output explanation. An extensive empirical study on various real-world datasets demonstrates that our method achieves excellent or on-par results in comparison to the state-of-the-art approaches as well as several baselines.Comment: Accepted and presented at ICDM 202

A modified Learn++.NSE algorithm for dealing with concept drift

© 2014 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. Concept drift is a very pervasive phenomenon in real world applications. By virtue of variety change types of concept drift, it makes more difficult for learning algorithm to track the concept drift very closely. Learn++.NSE is an incremental ensemble learner without any assumption on change type of concept drift. Even though it has good performance on handling concept drift, but it costs high computation and needs more time to recover from accuracy drop. This paper proposed a modified Learn++.NSE algorithm. During learning instances in data stream, our algorithm first identifies where and when drift happened, then uses instances accumulated by drift detection method to create a new base classifier, and finally organized all existing classifiers based on Learn++.NSE weighting mechanism to update ensemble learner. This modified algorithm can reduce high computation cost without any performance drop and improve the accuracy recover speed when drift happened