A5089247128- Data Stream Mining Techniques
- Machine Learning and Data Classification
- Ethics and Social Impacts of AI
- Imbalanced Data Classification Techniques
- Explainable Artificial Intelligence (XAI)
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Sentiment Analysis and Opinion Mining
- Spam and Phishing Detection
- Complex Network Analysis Techniques
- Advanced Clustering Algorithms Research
- Domain Adaptation and Few-Shot Learning
- Data Mining Algorithms and Applications
- Topic Modeling
- Advanced Text Analysis Techniques
- Recommender Systems and Techniques
- Data Management and Algorithms
- Mobile Crowdsensing and Crowdsourcing
- Network Security and Intrusion Detection
- Privacy-Preserving Technologies in Data
- Scientific Computing and Data Management
- Web Data Mining and Analysis
- Isotope Analysis in Ecology
- Advanced Image and Video Retrieval Techniques
- Semantic Web and Ontologies
Universität der Bundeswehr München
2022-2024
Freie Universität Berlin
2021-2023
University of Calabria
2023
Universitat Politècnica de València
2023
California State University System
2023
L3S Research Center
2017-2022
Leibniz University Hannover
2016-2021
Leibniz University of Applied Sciences
2019-2020
Gottfried-Wilhelm-Leibniz-Gesellschaft
2019
Ludwig-Maximilians-Universität München
2012-2016
Abstract Artificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions that have far‐reaching impact on individuals and society. Their might affect everyone, everywhere, anytime, entailing concerns about potential human rights issues. Therefore, it is necessary move beyond traditional AI algorithms optimized for predictive performance embed ethical legal principles in their design, training, deployment ensure social good while still benefiting from the huge of...
Abstract As decision‐making increasingly relies on machine learning (ML) and (big) data, the issue of fairness in data‐driven artificial intelligence systems is receiving increasing attention from both research industry. A large variety fairness‐aware ML solutions have been proposed which involve fairness‐related interventions algorithms, and/or model outputs. However, a vital part proposing new approaches evaluating them empirically benchmark datasets that represent realistic diverse...
Automated data-driven decision-making systems are ubiquitous across a wide spread of online as well offline services. These systems, depend on sophisticated learning algorithms and available data, to optimize the service function for decision support assistance. However, there is growing concern about accountability fairness employed models by fact that often historic data intrinsically discriminatory, i.e., proportion members sharing one or more sensitive attributes higher than in...
Data stream clustering is a hot research area due to the abundance of data streams collected nowadays and need for understanding acting upon such sort data. Unsupervised learning (clustering) comprises one most popular mining tasks gaining insights into Clustering challenging task, while over involves additional challenges as single pass constraint raw fast response. Moreover, dealing with an infinite changing implies that model extracted also subject evolution time. Several surveys exist...
The widespread use of ML-based decision making in domains with high societal impact such as recidivism, job hiring and loan credit has raised a lot concerns regarding potential discrimination. In particular, certain cases it been observed that ML algorithms can provide different decisions based on sensitive attributes gender or race therefore lead to Although, several fairness-aware approaches have proposed, their focus largely preserving the overall classification accuracy while improving...
Automated decision making based on big data and machine learning (ML) algorithms can result in discriminatory decisions against certain protected groups defined upon personal like gender, race, sexual orientation etc. Such designed to discover patterns might not only pick up any encoded societal biases the training data, but even worse, they reinforce such resulting more severe discrimination. The majority of thus far proposed fairness-aware approaches focus solely pre-, in- or...
Nowadays, WWW brings overwhelming variety of choices to consumers. Recommendation systems facilitate the selection by issuing recommendations them. Recommendations for users, or groups, are determined considering users similar in question. Scanning whole database locating though, is expensive. Existing approaches build cluster models employing full-dimensional clustering find sets users. As datasets we deal with high-dimensional and incomplete, not best option. To this end, explore...
AI-driven decision-making can lead to discrimination against certain individuals or social groups based on protected characteristics/attributes such as race, gender, age. The domain of fairness-aware machine learning focuses methods and algorithms for understanding, mitigating, accounting bias in AI/ML models. Still, thus far, the vast majority proposed assess fairness a single attribute, e.g. only gender race. In reality, though, human identities are multi-dimensional, occur more than one...
Sentiment analysis is an important task in order to gain insights over the huge amounts of opinions that are generated social media on a daily basis. Although there lot work sentiment analysis, no many datasets available which one can use for developing new methods and evaluation. To best our knowledge, largest dataset TSentiment [8], 1.6 millions machine-annotated tweets covering period about 3 months 2009. This however too short therefore insufficient study heterogeneous, fast evolving...
Abstract Class imbalance poses a major challenge for machine learning as most supervised models might exhibit bias towards the majority class and under-perform in minority class. Cost-sensitive tackles this problem by treating classes differently, formulated typically via user-defined fixed misclassification cost matrix provided input to learner. Such parameter tuning is challenging task that requires domain knowledge moreover, wrong adjustments lead overall predictive performance...
AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their might affect everyone, everywhere anytime, entailing concerns about potential human rights issues. Therefore, it is necessary move beyond traditional AI algorithms optimized for predictive performance embed ethical legal principles in their design, training deployment ensure social good while still benefiting from the huge of technology. The goal this survey...
Two of the most popular approaches for dealing with big data are distributed computing and stream mining. In this paper, we incorporate both in order to bring a competitive clustering algorithm, namely CluStream, into modern framework computing, namely, Apache Spark. CluStream is one that introduced online-offline mining process: online phase summarizes through statistical summaries offline generates final clusters upon these summaries. We obtain scalable method which open source can be used...