Incremental learning for large scale classification systems

Athanasios Alexopoulos; Andreas Kanavos; Konstantinos Giotopoulos; Alaa Mohasseb; Mohamed Bader-El-Den; Athanasios Tsakalidis

doi:10.1007/978-3-319-92016-0_11

Incremental learning for large scale classification systems

Athanasios Alexopoulos, Andreas Kanavos, Konstantinos Giotopoulos, Alaa Mohasseb, Mohamed Bader-El-Den, Athanasios Tsakalidis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

One of the main characteristics of our time is the growth of the data volumes. We collect data literally from everywhere; smart phones, smart devices, social media and the health care system, which defines a small portion of the sources of the big data. The big data growth poses two main difficulties, storing and processing them. For the former, there are certain new technologies that enable us to store large amounts of data in a fast and reliable way. For the latter, new application frameworks have been developed. In this paper, we perform classification analysis using Apache Spark in one real dataset. The classification algorithms that we have used are multiclass, and we are going to examine the effect of the dataset size and input features on the classification results.

Original language	English
Title of host publication	Artificial Intelligence Applications and Innovations
Subtitle of host publication	AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, May 25-27, 2018, Proceedings
Editors	Lazaros Iliadis, Ilias Maglogiannis, Vassilis Plagianakos
Publisher	Springer
Pages	112-122
Number of pages	11
ISBN (Electronic)	978-3-319-92016-0
ISBN (Print)	978-3-319-92015-3
DOIs	https://doi.org/10.1007/978-3-319-92016-0_11
Publication status	Published - Jun 2018
Event	14th International Conference on Artificial Intelligence Applications and Innovations - Rhodes, Greece Duration: 25 May 2018 → 27 May 2018 http://easyconferences.eu/aiai2018/

Publication series

Name	IFIP Advances in Information and Communication Technology
Publisher	Springer
Volume	520
ISSN (Print)	1868-4238
ISSN (Electronic)	1868-422X

Conference

Conference	14th International Conference on Artificial Intelligence Applications and Innovations
Abbreviated title	AIAI 2018
Country/Territory	Greece
City	Rhodes
Period	25/05/18 → 27/05/18
Internet address	http://easyconferences.eu/aiai2018/

Access to Document

10.1007/978-3-319-92016-0_11

Incremental Learning for Large Scale Classification Systems
he final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-319-92016-0_11.
Accepted author manuscript (Post-print), 258 KB

Cite this

Alexopoulos, A., Kanavos, A., Giotopoulos, K., Mohasseb, A., Bader-El-Den, M., & Tsakalidis, A. (2018). Incremental learning for large scale classification systems. In L. Iliadis, I. Maglogiannis, & V. Plagianakos (Eds.), Artificial Intelligence Applications and Innovations: AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, May 25-27, 2018, Proceedings (pp. 112-122). (IFIP Advances in Information and Communication Technology; Vol. 520). Springer. https://doi.org/10.1007/978-3-319-92016-0_11

Alexopoulos, Athanasios ; Kanavos, Andreas ; Giotopoulos, Konstantinos et al. / Incremental learning for large scale classification systems. Artificial Intelligence Applications and Innovations: AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, May 25-27, 2018, Proceedings. editor / Lazaros Iliadis ; Ilias Maglogiannis ; Vassilis Plagianakos. Springer, 2018. pp. 112-122 (IFIP Advances in Information and Communication Technology).

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title = "Incremental learning for large scale classification systems",

abstract = "One of the main characteristics of our time is the growth of the data volumes. We collect data literally from everywhere; smart phones, smart devices, social media and the health care system, which defines a small portion of the sources of the big data. The big data growth poses two main difficulties, storing and processing them. For the former, there are certain new technologies that enable us to store large amounts of data in a fast and reliable way. For the latter, new application frameworks have been developed. In this paper, we perform classification analysis using Apache Spark in one real dataset. The classification algorithms that we have used are multiclass, and we are going to examine the effect of the dataset size and input features on the classification results.",

author = "Athanasios Alexopoulos and Andreas Kanavos and Konstantinos Giotopoulos and Alaa Mohasseb and Mohamed Bader-El-Den and Athanasios Tsakalidis",

year = "2018",

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doi = "10.1007/978-3-319-92016-0_11",

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booktitle = "Artificial Intelligence Applications and Innovations",

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Alexopoulos, A, Kanavos, A, Giotopoulos, K, Mohasseb, A , Bader-El-Den, M & Tsakalidis, A 2018, Incremental learning for large scale classification systems. in L Iliadis, I Maglogiannis & V Plagianakos (eds), Artificial Intelligence Applications and Innovations: AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, May 25-27, 2018, Proceedings. IFIP Advances in Information and Communication Technology, vol. 520, Springer, pp. 112-122, 14th International Conference on Artificial Intelligence Applications and Innovations, Rhodes, Greece, 25/05/18. https://doi.org/10.1007/978-3-319-92016-0_11

Incremental learning for large scale classification systems. / Alexopoulos, Athanasios; Kanavos, Andreas; Giotopoulos, Konstantinos et al.
Artificial Intelligence Applications and Innovations: AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, May 25-27, 2018, Proceedings. ed. / Lazaros Iliadis; Ilias Maglogiannis; Vassilis Plagianakos. Springer, 2018. p. 112-122 (IFIP Advances in Information and Communication Technology; Vol. 520).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Incremental learning for large scale classification systems

AU - Alexopoulos, Athanasios

AU - Kanavos, Andreas

AU - Giotopoulos, Konstantinos

AU - Mohasseb, Alaa

AU - Bader-El-Den, Mohamed

AU - Tsakalidis, Athanasios

PY - 2018/6

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AB - One of the main characteristics of our time is the growth of the data volumes. We collect data literally from everywhere; smart phones, smart devices, social media and the health care system, which defines a small portion of the sources of the big data. The big data growth poses two main difficulties, storing and processing them. For the former, there are certain new technologies that enable us to store large amounts of data in a fast and reliable way. For the latter, new application frameworks have been developed. In this paper, we perform classification analysis using Apache Spark in one real dataset. The classification algorithms that we have used are multiclass, and we are going to examine the effect of the dataset size and input features on the classification results.

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Alexopoulos A, Kanavos A, Giotopoulos K, Mohasseb A , Bader-El-Den M, Tsakalidis A. Incremental learning for large scale classification systems. In Iliadis L, Maglogiannis I, Plagianakos V, editors, Artificial Intelligence Applications and Innovations: AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, May 25-27, 2018, Proceedings. Springer. 2018. p. 112-122. (IFIP Advances in Information and Communication Technology). Epub 2018 May 22. doi: 10.1007/978-3-319-92016-0_11

Incremental learning for large scale classification systems

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