Machine Learning and Principles and Practice of Knowledge Discovery in Databases
International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part II
Page
%P
-
Article
Open AccessContext discovery for anomaly detection
Contextual anomaly detection aims to identify objects that are anomalous only within specific contexts, while appearing normal otherwise. However, most existing methods are limited to a single context defined ...
-
Chapter and Conference Paper
Mind the Data, Measuring the Performance Gap Between Tree Ensembles and Deep Learning on Tabular Data
Recent machine learning studies on tabular data show that ensembles of decision tree models are more efficient and performant than deep learning models such as Tabular Transformer models. However, as we demons...
-
Chapter and Conference Paper
Data-Driven Explainable Artificial Intelligence for Energy Efficiency in Short-Sea Ship**
The maritime industry is under pressure to increase energy efficiency for climate change mitigation. Navigational data, combining vessel operational and environmental measurements from onboard instruments and ...
-
Book and Conference Proceedings
-
Book and Conference Proceedings
Machine Learning and Principles and Practice of Knowledge Discovery in Databases
International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part I
-
Chapter and Conference Paper
KAFE: Knowledge and Frequency Adapted Embeddings
Word embeddings are widely used in several Natural Language Processing (NLP) applications. The training process typically involves iterative gradient updates of each word vector. This makes word frequency a ma...
-
Chapter and Conference Paper
Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters
The data shared over the Internet tends to originate from ubiquitous and autonomous sources such as mobile phones, fitness trackers, and IoT devices. Centralized and federated machine learning solutions repre...
-
Chapter and Conference Paper
Warranty Claim Rate Prediction Using Logged Vehicle Data
Early detection of anomalies, trends and emerging patterns can be exploited to reduce the number and severity of quality problems in vehicles. This is crucially important since having a good understanding of ...
-
Chapter and Conference Paper
Predicting Air Compressor Failures Using Long Short Term Memory Networks
We introduce an LSTM-based method for predicting compressor failures using aggregated sensory data, and evaluate it using historical information from over 1000 heavy duty vehicles during 2015 and 2016. The goa...
-
Article
Open AccessAn adaptive algorithm for anomaly and novelty detection in evolving data streams
In the era of big data, considerable research focus is being put on designing efficient algorithms capable of learning and extracting high-level knowledge from ubiquitous data streams in an online fashion. Whi...
-
Article
Agreeing to disagree: active learning with noisy labels without crowdsourcing
We propose a new active learning method for classification, which handles label noise without relying on multiple oracles (i.e., crowdsourcing). We propose a strategy that selects (for labeling) instances with...
-
Chapter and Conference Paper
Supporting Analytical Reasoning
In the era of big data, it is imperative to assist the human analyst in the endeavor to find solutions to ill-defined problems, i.e. to “detect the expected and discover the unexpected” [23]. To their aid, a plet...
