Knowledge Discovery from Data Streams

Gama, João; Ganguly, Auroop R.; Omitaomu, Olufemi A.; Vatsavai, Ranga Raju; Gaber, Mohamed Medhat

doi:10.1201/ebk1439826119

Cited by 563 publications

(244 citation statements)

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“…To assess the effectiveness of the dynamic mode of the investigated classifiers, we applied an experimental procedure based on the methodology which is characteristic of data stream classification. Namely, the test-then-update procedure that uses data chunks was employed (Gama, 2010). The evaluation was conducted as follows.…”

Section: 12mentioning

confidence: 99%

A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier

Trajdos

Kurzyński

2016

International Journal of Applied Mathematics and Computer Science

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Nowadays, multiclassifier systems (MCSs) are being widely applied in various machine learning problems and in many different domains. Over the last two decades, a variety of ensemble systems have been developed, but there is still room for improvement. This paper focuses on developing competence and interclass cross-competence measures which can be applied as a method for classifiers combination. The cross-competence measure allows an ensemble to harness pieces of information obtained from incompetent classifiers instead of removing them from the ensemble. The cross-competence measure originally determined on the basis of a validation set (static mode) can be further easily updated using additional feedback information on correct/incorrect classification during the recognition process (dynamic mode). The analysis of computational and storage complexity of the proposed method is presented. The performance of the MCS with the proposed cross-competence function was experimentally compared against five reference MCSs and one reference MCS for static and dynamic modes, respectively. Results for the static mode show that the proposed technique is comparable with the reference methods in terms of classification accuracy. For the dynamic mode, the system developed achieves the highest classification accuracy, demonstrating the potential of the MCS for practical applications when feedback information is available.

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Section: 12mentioning

confidence: 99%

A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier

Trajdos

Kurzyński

2016

International Journal of Applied Mathematics and Computer Science

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“…Driven by the work in stream mining [10], we use different types of aging mechanisms to define the way that the historical information (in form of ratings) is incorporated in the recommendation process. Aging in streams is typically implemented through the notion of windows, which define which part of the stream is active at each time point and thus could be used for the computations.…”

Section: Fresh-based Recommendationsmentioning

confidence: 99%

A Framework for Time-Aware Recommendations

Stefanidis

Ntoutsi

Nørvåg

et al. 2012

Lecture Notes in Computer Science

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Abstract. Recently, recommendation systems have received significant attention. However, most existing approaches focus on recommending items of potential interest to users, without taking into consideration how temporal information influences the recommendations. In this paper, we argue that time-aware recommendations need to be pushed in the foreground. We introduce an extensive model for time-aware recommendations from two perspectives. From a fresh-based perspective, we propose using a suite of aging schemes towards making recommendations mostly depend on fresh and novel user preferences. From a context-based perspective, we focus on providing different suggestions under different temporal specifications. The proposed strategies are experimentally evaluated using real movies ratings.

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“…However, smooth changes are more difficult to be identified. At least in the initial phases, smooth changes can easily be confused with noise [14]. Since noise and examples from another distribution are differentiated by permanence, the detection of a smooth change in an early phase, tough to accomplish, is of foremost interest.…”

Section: Distribution Changesmentioning

confidence: 99%

Fading histograms in detecting distribution and concept changes

Sebastião

Gama

Mendonça

2017

Int J Data Sci Anal

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The remarkable number of real applications under dynamic scenarios is driving a novel ability to generate and gather information. Nowadays, a massive amount of information is generated at a high-speed rate, known as data streams. Moreover, data are collected under evolving environments. Due to memory restrictions, data must be promptly processed and discarded immediately. Therefore, dealing with evolving data streams raises two main questions: (i) how to remember discarded data? and (ii) how to forget outdated data? To maintain an updated representation of the time-evolving data, this paper proposes fading histograms. Regarding the dynamics of nature, changes in data are detected through a windowing scheme that compares data distributions computed by the fading histograms: the adaptive cumulative windows model (ACWM). The online monitoring of the distance between data distributions is evaluated using a dissimilarity measure based on the asymmetry of the Kullback-Leibler divergence. The experimental results support the ability of fading histograms in providing an updated representation of data. Such property works in favor of detecting distribution changes with smaller detection delay time when compared with standard histograms. With respect to the detection of concept changes, the ACWM is compared with 3 known algorithms taken from the literature, using artificial data and using public data sets, presenting better results. Furthermore, we the proposed method was extended for multidimensional and the experiments performed show the ability of the ACWM for detecting distribution changes in these settings.

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Knowledge Discovery from Data Streams

Cited by 563 publications

References 0 publications

A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier

A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier

A Framework for Time-Aware Recommendations

Fading histograms in detecting distribution and concept changes

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