Online Methods of Learning in Occurrence of Concept Drift

Mittal, Veena; Kashyap, Indu

doi:10.5120/20614-3280

Cited by 14 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The information captured by online behavior can thus change over time and lead the model's performance to drop [29]. Although a number of control mechanisms can be put into place (e.g., online learning [29,30]), understanding which behavioral features have a (large) impact on a model's classifications through explanations can help domain experts make sound statements on the expected lifetime of a model and its sensitivity to rapidly changing technological indicators and digital behavior. For example, the type of mobile phone applications that people use might change more rapidly compared to the genres of movies people watch or the type of places they visit on the weekend, which reflect more 'stable' behavior.…”

Section: Model Improvementmentioning

confidence: 99%

Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records

Ramon

Farrokhnia²,

Matz

et al. 2021

Information

View full text Add to dashboard Cite

Every step we take in the digital world leaves behind a record of our behavior; a digital footprint. Research has suggested that algorithms can translate these digital footprints into accurate estimates of psychological characteristics, including personality traits, mental health or intelligence. The mechanisms by which AI generates these insights, however, often remain opaque. In this paper, we show how Explainable AI (XAI) can help domain experts and data subjects validate, question, and improve models that classify psychological traits from digital footprints. We elaborate on two popular XAI methods (rule extraction and counterfactual explanations) in the context of Big Five personality predictions (traits and facets) from financial transactions data (N = 6408). First, we demonstrate how global rule extraction sheds light on the spending patterns identified by the model as most predictive for personality, and discuss how these rules can be used to explain, validate, and improve the model. Second, we implement local rule extraction to show that individuals are assigned to personality classes because of their unique financial behavior, and there exists a positive link between the model’s prediction confidence and the number of features that contributed to the prediction. Our experiments highlight the importance of both global and local XAI methods. By better understanding how predictive models work in general as well as how they derive an outcome for a particular person, XAI promotes accountability in a world in which AI impacts the lives of billions of people around the world.

show abstract

Section: Model Improvementmentioning

confidence: 99%

Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records

Ramon

Farrokhnia²,

Matz

et al. 2021

Information

View full text Add to dashboard Cite

show abstract

“…For example, the content mismatch between old and new data will create a concept shift. Four different conceptual shifts are introduced as sudden, gradual, repetitive and incremental [22]. The different levels of detail of event logs is another challenge that should not be ignored [44].…”

Section: Process Mining Problemsmentioning

confidence: 99%

Process Mining in Manufacturing: A Literature Review

Yurtay

2022

Sakarya University Journal of Computer and Information Sciences

View full text Add to dashboard Cite

Process mining in manufacturing is a newly expanding field of research in the application of data mining and machine learning techniques and the focus of business processes. Although it is an exciting subject of the recent past and business processes, sufficient research has not been done. Decision support systems such as enterprise resource planning, customer relationship management, and management information systems store the most valuable resource data of process details and event logs. In the advanced information systems of tomorrow, the process management, analysis, and modelling functions of modern enterprises will take their place as a necessity. As a requirement, the fundamental purpose of process mining in production is to refine data from event logs, automatically create process models, compare models with event logs, and improve and make development continuous. Our work is to contribute to application and research studies by drawing attention to process mining in the context of production. It is based on the literature review and primary stages of business process mining publications in the last decade with a production focus. An overview is discussed as a roadmap for future research with meaningful results.

show abstract

“…The stationary data generators generate data continuously with respect to time with uniform data distribution. A data that is generated continuously with time is termed as data stream [2][3][4][5].…”

Section: Data Stream and Concept Driftsmentioning

confidence: 99%

Performance Evaluation and Estimation for Concept Drifting Data Stream Mining

Mittal¹,

Kashyap²

2018

IJCA

Self Cite

View full text Add to dashboard Cite

In Machine learning tasks, mainly in classification problems, the performance measures are of major concern in order to determine and compare the performance of classification methods. In classification problem the accuracy of the classifier is one of the most important performance measures commonly used. However, computing performance in the dynamic environment learning offered for concept drifting data streams also requires some performance considerations as compared to classification tasks in static environment. Furthermore, the learning and testing strategies widely used for training and testing of classifiers of static environments cannot be utilized efficiently to meet the requirements of concept drifting data stream mining as the main requirement of online leaning is to perform one pass incremental learning on large datasets conversely to the allowable iterative learning in static environment with small datasets. This paper describes some important performance measures and learning and testing strategies pertaining to online and incremental learning in the presence of concept drifting data streams. Furthermore, this paper also presents performance measures of drift detection methods widely used as an explicit component in many concept drifting data stream mining algorithms.

show abstract

Online Methods of Learning in Occurrence of Concept Drift

Cited by 14 publications

References 14 publications

Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records

Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records

Process Mining in Manufacturing: A Literature Review

Performance Evaluation and Estimation for Concept Drifting Data Stream Mining

Contact Info

Product

Resources

About