A survey on concept drift adaptation

Gama, João; Žliobaitė, Indrė; Bifet, Albert; Pechenizkiy, Mykola; Bouchachia, Abdelhamid

doi:10.1145/2523813

Cited by 2,330 publications

(1,942 citation statements)

References 150 publications

(203 reference statements)

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“…Research has shown that exploiting time in the rating prediction computation can improve prediction accuracy, due to concept drift; concept drift is the phenomenon when the relation between the input data and the target variable changes over time [6]. Change of interests [26], [6], is a typical example of concept drift.…”

Section: Related Workmentioning

confidence: 99%

“…Change of interests [26], [6], is a typical example of concept drift. To this end, Zliobaite et al [27] develop an intelligent approach for sales prediction, which uses a mechanism for model switching, depending on the sales behavior of a product.…”

Section: Related Workmentioning

confidence: 99%

“…Works such as [18], [19] and [43] also assert that a tradeoff between coverage and accuracy exists, and in order to obtain a single measure for rating the suitability of each algorithm, the harmonic mean (HM) of these measures can be adopted; this is analogous to the goal of maximizing the HM of precision and recall -termed the F1 measure -in information retrieval [44]). Towards this direction, we adopt the following formula introduced in [43]: (6) where aAlg is a rating prediction algorithm and Alg is the set of all algorithms participating in the evaluation. For the DA clusters algorithm, different settings for the FACTOR parameter are considered as different algorithms in the context of the evaluation.…”

Section: The Amazon "Videogames" Datasetmentioning

confidence: 99%

“…Insofar, while many efforts have been made to improve the CF prediction accuracy, and the aspect of changes in users' interests has been extensively studied ( [6] provides a comprehensive review), the issue of shifts in rating practices has not received adequate attention. Margaris and Vassilakis [7] introduce the concept of dynamic user rating averages which follow the users' marking practices shifts and present two alternative algorithms, namely the DA vicinity and DA previous algorithms, for computing a user's dynamic averages.…”

Section: Introductionmentioning

confidence: 99%

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Using Time Clusters for Following Users’ Shifts in Rating Practices

Margaris

Vassilakis

2017

CSIMQ

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Abstract. Users that enter ratings for items follow different rating practices, in the sense that, when rating items, some users are more lenient, while others are stricter. This aspect is taken into account by the most widely used similarity metric in user-user collaborative filtering, namely, the Pearson Correlation, which adjusts each individual user rating by the mean value of the ratings entered by the specific user, when computing similarities. However, a user's rating practices change over time, i.e. a user could start as strict and subsequently become lenient or vice versa. In that sense, the practice of using a single mean value for adjusting users' ratings is inadequate, since it fails to follow such shifts in users' rating practices, leading to decreased rating prediction accuracy. In this work, we address this issue by using the concept of dynamic averages introduced earlier and we extend earlier work by (1) introducing the concept of rating time clusters and (2) presenting a novel algorithm for calculating dynamic user averages and exploiting them in useruser collaborative, filtering implementations. The proposed algorithm incorporates the aforementioned concept and is able to follow more successfully shifts in users' rating practices. It has been evaluated using numerous datasets, and has been found to introduce significant gains in rating prediction accuracy, while outperforming the dynamic average computation approaches that are presented earlier.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: The Amazon "Videogames" Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Time Clusters for Following Users’ Shifts in Rating Practices

Margaris

Vassilakis

2017

CSIMQ

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“…In text analysis, the term concept drift is used to indicate a change in the context in which a concept is used [Wang et al 2010] or a change in the statistical properties of a concept as predicted by a machine learning model [Tsymbal 2004;Gama et al 2014]. Here, we are interested in the evolution of the textual context in which anatomical concepts are used in the literature, as measured by examining the words surrounding controlled vocabulary terms.…”

Section: Introductionmentioning

confidence: 99%

Evolution of anatomical concept usage over time: Mining 200 years of biodiversity literature

Manda

Vision

2017

Preprint

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The scientific literature contains an historic record of the changing ways in which we describe the world. Shifts in understanding of scientific concepts are reflected in the introduction of new terms and the changing usage and context of existing ones. We conducted an ontology-based temporal data mining analysis of biodiversity literature from the 1700s to 2000s to quantitatively measure how the context of usage for vertebrate anatomical concepts has changed over time. The corpus of literature was divided into nine non-overlapping time periods with comparable amounts of data and context vectors of anatomical concepts were compared to measure the magnitude of concept drift both between adjacent time periods and cumulatively relative to the initial state. Surprisingly, we found that while anatomical concept drift between adjacent time periods was substantial (55% to 68%), it was of the same magnitude as cumulative concept drift across multiple time periods. Such a process, bound by an overall mean drift, fits the expectations of a mean-reverting process.

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Bayesian Graphical Models

Jensen

Nielsen

2016

Wiley StatsRef: Statistics Reference Online

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Mathematically, a Bayesian graphical model is a compact representation of the joint probability distribution for a set of variables. The most frequently used type of Bayesian graphical models are Bayesian networks. The structural part of a Bayesian graphical model is a graph consisting of nodes and edges. The nodes represent variables, which may be either discrete or continuous. An edge between two nodes and indicates a direct influence between the state of and the state of , which in some domains can also be interpreted as a causal relation. The widespread use of Bayesian networks is largely due to the availability of efficient inference algorithms for answering probabilistic queries about the states of the variables in the network. Furthermore, to support the construction of Bayesian network models, learning algorithms are also available. We give an overview of the Bayesian network formalism and some of the algorithmic developments in the area.

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A survey on concept drift adaptation

Cited by 2,330 publications

References 150 publications

Using Time Clusters for Following Users’ Shifts in Rating Practices

Using Time Clusters for Following Users’ Shifts in Rating Practices

Evolution of anatomical concept usage over time: Mining 200 years of biodiversity literature

Bayesian Graphical Models

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