MLDM-SN 2015 - 2nd International Workshop on Machine Learning and Data Mining for Sensor Networks

As the applications for machine learning expand into other areas, the need for high-quality machine learning methods constantly grows. Additionally, there is a need for interpretable models as researchers want to grasp the models and get a sense of how the sensor information is combined in the model.
However, sensor data poses a number of unique challenges for machine learning. Ranging from missing values, unreliable measurements, missing calibration to high spatial diversity. Most challenges have not been addressed with a focus on real-world sensor data. It is our belief that a discussion will help foster new results in the intersection of both communities.
Topic Areas of Interest
Real-time machine learning
Iterative machine learning
Multi-target learning
Generating data analysis pipelines
Evaluation of machine learning models tailored to sensor data
Data extraction from sensor networks
Data conversion and calibration issues
Meta-learning, e.g., learning to adjust the analysis pipeline automatically
Interpretable models, e.g., Rule Learning or Decision Tree Learning
Generating high-quality data sets
Data quality issues
Dealing with missing and low quality data
Feature Engineering with a focus on sensor data features
Feature weighting and combination
Generating high-quality features from sensor data