Roberto Corizzo

1.8k citations

74 papers · 1.3k · 1 hit paper · h-index 19

Impact in

Artificial Intelligence top 5%
- Anomaly Detection Techniques and Applications
- Data Stream Mining Techniques
- Solar Radiation and Photovoltaics
Signal Processing top 5%
- Time Series Analysis and Forecasting

Papers in

Artificial Intelligence 54
- Anomaly Detection Techniques and Applications 24
- Data Stream Mining Techniques 13
- Topic Modeling 6
- Domain Adaptation and Few-Shot Learning 5
Signal Processing 15
- Time Series Analysis and Forecasting 12

Co-authors: Nathalie Japkowicz (29 shared papers)Michelangelo Ceci (20 shared papers)Eftim Zdravevski (9 shared papers)Donato Malerba (9 shared papers)Colin Bellinger (7 shared papers)Petre Lameski (6 shared papers)Bartosz Krawczyk (3 shared papers)Aleksandra Rashkovska (4 shared papers)
Journals: Machine Learning (5 papers)IEEE Access (2 papers)IEEE Transactions on Neural Networks and Learning Systems (2 papers)Information Sciences (2 papers)Sensors (2 papers)
Partner nations: United States Italy Poland

In The Last Decade

Roberto Corizzo

66 papers receiving 1.2k citations

Roberto Corizzo's Hit Papers

align trajectories log scale

The class imbalance problem in deep learning 2022 · 163 citations

What are hit papers?

Paper score: A paper's citations measured against the top-1% bar of its own field and year: the square root of citations ÷ bar, so 1 means right at the bar and 2 means four times its citations. The bar blends the citation counts that enter the top 1% of the paper's subfields in its year (weighted 89.5%), of those subfields over all years (0.5%) and of its year across all fields (10%); a subfield and year with fewer than 400 papers takes its year's bar. Papers from 2026 are not scored yet: in an unfinished year, when a paper appeared matters more than how it is cited.

Hit paper: A paper cited at least 1.5× the top-1% bar of its field and year: a paper score of at least √1.5.

2022 Machine Learning

Peers

Countries citing papers authored by Roberto Corizzo

Since Specialization

Citations

This map shows the geographic impact of Roberto Corizzo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Roberto Corizzo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roberto Corizzo more than expected).

Fields of papers citing papers by Roberto Corizzo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Roberto Corizzo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Roberto Corizzo. The network helps show where Roberto Corizzo may publish in the future.

Co-authors

The 25 scholars most cited alongside Roberto Corizzo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Roberto Corizzo Line = papers co-authored together Roberto Corizzo links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 74 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	The class imbalance problem in deep learning Machine Learning ·Kushankur Ghosh, Colin Bellinger, Roberto Corizzo, Paula Alexandra de Oliveira Branco, Bartosz Krawczyk, Nathalie Japkowicz Hit paper breakdown →	2022	163
2	Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks Remote Sensing ·Jovan Kalajdjieski, Eftim Zdravevski, Roberto Corizzo, Petre Lameski, Slobodan Kalajdziski, Ivan Miguel Pires, Nuno M. García, Vladimir Trajkovik	2020	90
3	Deep Learning for Feature Extraction in Remote Sensing: A Case-Study of Aerial Scene Classification Sensors ·Biserka Petrovska, Eftim Zdravevski, Petre Lameski, Roberto Corizzo, Ivan Štajduhar, Jonatan Lerga	2020	86
4	Predictive Modeling of PV Energy Production: How to Set Up the Learning Task for a Better Prediction? IEEE Transactions on Industrial Informatics ·Michelangelo Ceci, Roberto Corizzo, Fabio Fumarola, Donato Malerba, Aleksandra Rashkovska	2016	79
5	Multi-aspect renewable energy forecasting Information Sciences ·Roberto Corizzo, Michelangelo Ceci, Hadi Fanaee‐T, João Manuel Portela da Gama	2020	66
6	Spatial autocorrelation and entropy for renewable energy forecasting Data Mining and Knowledge Discovery ·Michelangelo Ceci, Roberto Corizzo, Donato Malerba, Aleksandra Rashkovska	2019	61
7	Anomaly Detection and Repair for Accurate Predictions in Geo-distributed Big Data Big Data Research ·Roberto Corizzo, Michelangelo Ceci, Nathalie Japkowicz	2019	49
8	ECHAD: Embedding-Based Change Detection From Multivariate Time Series in Smart Grids IEEE Access ·Michelangelo Ceci, Roberto Corizzo, Nathalie Japkowicz, Paolo Mignone, Gianvito Pio	2020	46
9	Scalable auto-encoders for gravitational waves detection from time series data Expert Systems with Applications ·Roberto Corizzo, Michelangelo Ceci, Eftim Zdravevski, Nathalie Japkowicz	2020	43
10	DENCAST: distributed density-based clustering for multi-target regression Journal Of Big Data ·Roberto Corizzo, Gianvito Pio, Michelangelo Ceci, Donato Malerba	2019	41
11	Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing Applied Sciences ·Biserka Petrovska, Tatjana Atanasova-Pacemska, Roberto Corizzo, Paolo Mignone, Petre Lameski, Eftim Zdravevski	2020	40
12	Multi-Horizon Air Pollution Forecasting with Deep Neural Networks Sensors ·Mirche Arsov, Eftim Zdravevski, Petre Lameski, Roberto Corizzo, Nikola Koteli, Sasho Gramatikov, Kosta Mitreski, Vladimir Trajkovik	2021	32
13	Spark-GHSOM: Growing Hierarchical Self-Organizing Map for large scale mixed attribute datasets Information Sciences ·Ameya Mohan Malondkar, Roberto Corizzo, Iluju Kiringa, Michelangelo Ceci, Nathalie Japkowicz	2018	32
14	Stock market prediction with time series data and news headlines: a stacking ensemble approach Journal of Intelligent Information Systems ·Roberto Corizzo, Jacob Rosén	2023	29
15	Undersampling with Support Vectors for Multi-Class Imbalanced Data Classification NPARC ·Bartosz Krawczyk, Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz	2021	22
16	Explainable image analysis for decision support in medical healthcare 2021 IEEE International Conference on Big Data (Big Data) ·Roberto Corizzo, Yohan Dauphin, Colin Bellinger, Eftim Zdravevski, Nathalie Japkowicz	2021	21
17	Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review Biology ·Petar Tonkovic, Slobodan Kalajdziski, Eftim Zdravevski, Petre Lameski, Roberto Corizzo, Ivan Miguel Pires, Nuno M. García, Tatjana Lončar-Turukalo	2020	21
18	Big Data Techniques For Supporting Accurate Predictions of Energy Production From Renewable Sources CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) ·Michelangelo Ceci, Roberto Corizzo, Fabio Fumarola, Michele Ianni, Donato Malerba, Gaspare Maria, Elio Masciari, Marco Oliverio	2014	20
19	Short-term air pollution forecasting based on environmental factors and deep learning models Annals of Computer Science and Information Systems ·Mirche Arsov, Eftim Zdravevski, Petre Lameski, Roberto Corizzo, Nikola Koteli, Kosta Mitreski, Vladimir Trajkovik	2020	19
20	Calibrated Resampling for Imbalanced and Long-Tails in Deep Learning Lecture notes in computer science ·Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz	2021	19

About Roberto Corizzo

Roberto Corizzo is a scholar working on Artificial Intelligence, Signal Processing, Computer Networks and Communications, Management Science and Operations Research and Computer Vision and Pattern Recognition, having authored 74 papers that have together received 1.3k indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (24 papers), Data Stream Mining Techniques (13 papers), Time Series Analysis and Forecasting (12 papers), Network Security and Intrusion Detection (10 papers), Energy Load and Power Forecasting (9 papers), Topic Modeling (6 papers), Domain Adaptation and Few-Shot Learning (5 papers) and COVID-19 diagnosis using AI (5 papers). The work is most often cited by research in Artificial Intelligence (575 citations), Signal Processing (146 citations), Environmental Engineering (174 citations), Computational Mathematics (7 citations) and Media Technology (90 citations). Roberto Corizzo has collaborated with scholars based in United States, Italy and Poland. Frequent co-authors include Nathalie Japkowicz, Michelangelo Ceci, Eftim Zdravevski, Donato Malerba, Colin Bellinger, Petre Lameski, Bartosz Krawczyk, Aleksandra Rashkovska, Paula Alexandra de Oliveira Branco and Kushankur Ghosh. Their work appears in journals such as Machine Learning, IEEE Access, IEEE Transactions on Neural Networks and Learning Systems, Information Sciences and Sensors.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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Roberto Corizzo's Hit Papers

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