Machine learning and knowledge discovery in databases : Applied data science track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings. Yuxiao Dong, Nicolas Kourtellis, Barbara Hammer, Jose A. Lozano (eds.). Part IV /
The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but...
Saved in:
| Corporate Author: | |
|---|---|
| Other Authors: | , , , |
| Format: | Ebook |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer,
2021.
|
| Series: | Lecture notes in computer science. Lecture notes in artificial intelligence
Lecture notes in computer science ; 12978. LNCS sublibrary. Artificial intelligence |
| Subjects: | |
| Online Access: | Springer eBooks |
MARC
| LEADER | 00000czm a2200000Ii 4500 | ||
|---|---|---|---|
| 005 | 20221110003539.0 | ||
| 006 | m o d | ||
| 007 | cr cnu000|uu|| | ||
| 008 | 210916s2021 o o o ||0| 0 eng d | ||
| 011 | |a Z3950 Direct Search: Record 0 of 3 | ||
| 011 | |a Z9350 Search Query @attr 1=7 "9783030865139 9783030865146" | ||
| 011 | |a MARC Score : 10700(24250) : OK | ||
| 020 | |a 3030865134 |q Internet | ||
| 020 | |a 9783030865139 |q Internet | ||
| 020 | |a 3030865142 |q Internet | ||
| 020 | |a 9783030865146 |q Internet | ||
| 035 | |a (OCoLC)1268266960 | ||
| 035 | |a (EDS)EDS29769597 | ||
| 040 | |a GW5XE |b eng |e rda |c GW5XE |d OCLCO |d DKU |d EBLCP |d OCLCF |d Z5A | ||
| 050 | 4 | |a Q325.5 | |
| 082 | 0 | 4 | |a 006.31 |2 23 |
| 099 | |a 006.31 ECM | ||
| 111 | 2 | |a ECML PKDD (Conference) |d (2021 : |c Online) | |
| 245 | 1 | 0 | |a Machine learning and knowledge discovery in databases : |b Applied data science track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings. |n Part IV / |c Yuxiao Dong, Nicolas Kourtellis, Barbara Hammer, Jose A. Lozano (eds.). |
| 246 | 3 | |a ECML PKDD 2021 | |
| 246 | 3 | |a Applied data science track | |
| 264 | 1 | |a Cham, Switzerland : |b Springer, |c 2021. | |
| 300 | |a 1 online resource (xxxiv, 554 pages) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF | ||
| 490 | 1 | |a Lecture notes in artificial intelligence | |
| 490 | 1 | |a Lecture notes in computer science ; |v 12978 | |
| 490 | 1 | |a LNCS sublibrary, SL 7, Artificial intelligence | |
| 500 | |a "Unfortunately it had to be held online and we could only meet each other virtually."-- Preface. | ||
| 500 | |a Includes author index. | ||
| 505 | 0 | |a Intro -- Preface -- Organization -- Contents -- Part IV -- Anomaly Detection and Malware -- Anomaly Detection: How to Artificially Increase Your F1-Score with a Biased Evaluation Protocol -- 1 Introduction -- 2 Related Work -- 3 Issues When Using F1-Score and AVPR Metrics -- 3.1 Formalism and Problem Statement -- 3.2 Definition of the Metrics -- 3.3 Evaluation Protocols: Theory vs Practice -- 3.4 Metrics Sensitivity to the Contamination Rate of the Test Set -- 3.5 How to Artificially Increase Your F1-Score and AVPR -- 3.6 F1-Score Cannot Compare Datasets Difficulty -- 4 Call for Action -- | |
| 505 | 8 | |a 4.1 Use AUC -- 4.2 Do Not Waste Anomalous Samples -- 5 Conclusion -- References -- Mining Anomalies in Subspaces of High-Dimensional Time Series for Financial Transactional Data -- 1 Introduction -- 2 Related Work -- 3 Definitions and Notation -- 4 System Architecture -- 4.1 Subspace Searching Module -- 4.2 Discord Mining Module -- 4.3 Discussion -- 5 Evaluation -- 5.1 Alternative Approaches -- 5.2 Synthetic Data -- 5.3 Real-World Transactional Data -- 6 Conclusion -- References -- AIMED-RL: Exploring Adversarial Malware Examples with Reinforcement Learning -- 1 Introduction -- 2 Related Work -- | |
| 505 | 8 | |a 2.1 Reinforcement Learning -- 2.2 Further Approaches -- 3 AIMED-RL -- 3.1 Framework and Notation -- 3.2 Experimental Setting -- 3.3 Environment -- 4 Experimental Results -- 4.1 Diversity of Perturbations -- 4.2 Evasion Rate -- 5 Availability -- 6 Conclusion -- References -- Learning Explainable Representations of Malware Behavior -- 1 Introduction -- 2 Related Work -- 3 Problem Setting and Operating Environment -- 3.1 Network Events -- 3.2 Identification of Threats -- 3.3 Data Collection and Quantitative Analysis -- 4 Models -- 4.1 Architectures -- 4.2 Unsupervised Pre-training -- 5 Experiments -- | |
| 505 | 8 | |a 5.1 Hyperparameter Optimization -- 5.2 Malware-Classification Performance -- 5.3 Indicators of Compromise -- 6 Conclusion -- References -- Strategic Mitigation Against Wireless Attacks on Autonomous Platoons -- 1 Introduction -- 1.1 Related Work -- 2 Message Falsification Attacks Against Platoons -- 2.1 Vehicular Platoon Control Policy -- 2.2 Attack Model -- 2.3 Attack Detection Algorithm -- 3 Security Game-Based Mitigation Framework -- 3.1 Numerical Example -- 4 Simulation Setup -- 5 Simulation Results and Discussion -- 5.1 Realistic Driving Scenario -- 6 Conclusion -- References -- | |
| 505 | 8 | |a DeFraudNet: An End-to-End Weak Supervision Framework to Detect Fraud in Online Food Delivery -- 1 Introduction -- 2 Related Work -- 3 The Framework: DeFraudNet -- 3.1 Problem Definition -- 3.2 Fraud Detection Pipeline -- 4 Data and Feature Processing -- 4.1 Dataset -- 4.2 Feature Engineering -- 5 Label Generation -- 5.1 Generating Noisy Labels Using LFs -- 5.2 Snorkel Generative Model -- 5.3 Class-Specific Autoencoders for Denoising -- 6 Discriminator Models -- 6.1 Multi Layer Perceptron -- 6.2 LSTM Sequence Model -- 7 Deployment and Serving Infrastructure -- 8 Ablation Experiments. | |
| 520 | |a The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media. | ||
| 588 | 0 | |a Online resource; title from PDF title page (SpringerLink, viewed September 16, 2021). | |
| 650 | 0 | |a Machine learning |v Congresses. |9 736773 | |
| 650 | 0 | |a Data mining |v Congresses. |9 718631 | |
| 700 | 1 | |a Dong, Yuxiao, |e editor. |9 1005802 | |
| 700 | 1 | |a Kourtellis, Nicolas, |e editor. | |
| 700 | 1 | |a Hammer, Barbara, |d 1970- |e editor. |9 450403 | |
| 700 | 1 | |a Lozano, José A., |d 1968- |e editor. |9 450603 | |
| 776 | 1 | 8 | |w (OCoLC)1268574333 |
| 830 | 0 | |a Lecture notes in computer science. |p Lecture notes in artificial intelligence |9 236251 | |
| 830 | 0 | |a Lecture notes in computer science ; |v 12978. |9 291610 | |
| 830 | 0 | |a LNCS sublibrary. |n SL 7, |p Artificial intelligence |9 268157 | |
| 856 | 4 | 0 | |u https://ezproxy.aut.ac.nz/login?url=https://link.springer.com/10.1007/978-3-030-86514-6 |z Springer eBooks |x TEMPORARY ERM URL |
| 942 | |c EB |n 0 | ||
| 999 | |c 1677480 |d 1677480 | ||