Learning drifting concepts: Example selection vs. example weighting

This work presents an architecture for the development of on-line prediction models. The architecture defines unified modular environment based on three concepts from machine learning, these are: (i) ensemble methods, (ii) local learning, and (iii) meta learning. The three concepts are organised in a three layer hierarchy within the architecture. For the actual prediction making any data-driven predictive method such as artificial neural network, support vector machines, etc. can be implemented and plugged in. In addition to the predictive methods, data pre-processing methods can also be implemented as plug-ins. Models developed according to the architecture can be trained and operated in different modes. With regard to the training, the architecture supports the building of initial models based on a batch of training data, but if this data is not available the models can also be trained in incremental mode. In a scenario where correct target values are (occasionally) available during the run-time, the architecture supports life-long learning by providing several adaptation mechanisms across the three hierarchical levels. In order to demonstrate its practicality, we show how the issues of current soft sensor development and maintenance can be effectively dealt with by using the architecture as a construction plan for the development of adaptive soft sensing algorithms.

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“…-Moving window: [53] -Partial memory learning: [54] -Instance weighting and selection: [55] -Gradual forgetting: [56].…”

Section: Adaptation Mechanismsmentioning

confidence: 99%

Architecture for development of adaptive on-line prediction models

Kadlec

Gabrys

2009

Memetic Comp.

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“…In the context of learning data streams, some proposed algorithms are capable of dealing with gradual concept drift [23], some can handle abrupt concept drift [4,6,12,13,31], and some have the potential to cope with both types [36]. However, most of these methods are appropriate only for supervised environments in which the labels of data are fully known.…”

Section: Related Workmentioning

confidence: 99%

An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streams

2015

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Recent advances in storage and processing have provided the possibility of automatic gathering of information, which in turn leads to fast and continuous flows of data. The data which are produced and stored in this way are called data streams. Data streams are produced in large size, and much dynamism and have some unique properties which make them applicable to model many real data mining applications. The main challenge of streaming data is the occurrence of concept drift. In addition, regarding the costs of labeling of instances, it is often assumed that only a small fraction of instances are labeled. In this paper, we propose an ensemble algorithm to classify instances of non-stationary data streams in a semi-supervised environment. Furthermore, this method is intended to recognize recurring concept drifts of data streams. In the proposed algorithm, a pool of classifiers is maintained by the algorithm with each classifier being representative of one single concept. At first, a batch of instances is classified by the algorithm. Thereafter, some of these instances are labeled and this partially labeled batch is used to update the classifiers in the pool. This process repeats for consecutive batches of the streams. The main advantage of the algorithm is that it uses unlabeled instances as well as labeled ones in the learning task. Experimental results show the effectiveness of the proposed algorithm over the state-of-the-art methods, in different aspects.

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“…There exist several techniques: instance selection, instance weighting, and ensemble learning. Instance selection (Klinkenberg 2004) is the best-known technique and includes two methodologies: fixed window and adaptive window, where the model is regenerated using the last data batches. The instance selection technique suffers the problem of window size in case of fixed size and the pace of drift when adaptive windowing is adopted.…”

Section: Drift Handlingmentioning

confidence: 99%

Fuzzy classification in dynamic environments

Bouchachia

2010

Soft Comput

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The persistence and evolution of systems essentially depend on their adaptivity to new situations. As an expression of intelligence, adaptivity is a distinguishing quality of any system that is able to learn and to adjust itself in a flexible manner to new environmental conditions and such ability ensures self-correction over time as new events happen, new input becomes available, or new operational conditions occur. This requires self-monitoring of the performance in an ever-changing environment. The relevance of adaptivity is established in numerous domains and by versatile real-world applications. The present paper presents an incremental fuzzy rule-based system for classification purposes. Relying on fuzzy min-max neural networks, the paper explains how fuzzy rules can be continuously online generated to meet the requirements of non-stationary dynamic environments, where data arrives over long periods of time. The approach proposed to deal with an ambient intelligence application. The simulation results show its effectiveness in dealing with dynamic situations and its performance when compared with existing approaches.

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Learning drifting concepts: Example selection vs. example weighting

Cited by 357 publications

References 14 publications

Architecture for development of adaptive on-line prediction models

Architecture for development of adaptive on-line prediction models

An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streams

Fuzzy classification in dynamic environments

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