Data-driven modeling is an approach in energy systems modeling that has been gaining popularity. In data-driven modeling, machine learning methods such as linear regression, neural networks or decision-tree based methods are being applied. While these methods do not require domain knowledge, they are sensitive to data quality. Therefore, improving data quality in a dataset is beneficial for creating machine learning-based models. The improvement of data quality can be implemented through preprocessing methods. A selected type of preprocessing is feature engineering, which focuses on evaluating and improving the quality of certain features inside the dataset. Feature engineering methods include methods such as feature creation, feature expansion, or feature selection. In this work, a Python framework containing different feature engineering methods is presented. This framework contains different methods for feature creation, expansion and selection; in addition, methods for transforming or filtering data are implemented. The implementation of the framework is based on the Python library scikit-learn. The framework is demonstrated on a case study of a use case from energy demand prediction. A data-driven model is created including selected feature engineering methods. The results show an improvement in prediction accuracy through the engineered features.
Technische Universität Berlin（柏林工业大学）；BASF SE（巴斯夫欧洲公司）
This paper describes the autofeat Python library, which provides scikit-learn style linear regression and classification models with automated feature engineering and selection capabilities. Complex non-linear machine learning models, such as neural networks, are in practice often difficult to train and even harder to explain to non-statisticians, who require transparent analysis results as a basis for important business decisions. While linear models are efficient and intuitive, they generally provide lower prediction accuracies. Our library provides a multi-step feature engineering and selec…
Technical University of Munich（慕尼黑工业大学）；Software AG（Software AG公司）
In this paper, we present the current position of the research project ML-Quadrat, which aims to extend the methodology, modeling language and tool support of ThingML - an open source modeling tool for IoT/CPS - to address Machine Learning needs for the IoT applications. Currently, ThingML offers a modeling language and tool support for modeling the components of the system, their communication interfaces as well as their behaviors. The latter is done through state machines. However, we argue that in many cases IoT/CPS services involve system components and physical processes, whose behaviors …
Augmenting Biological Fitness Prediction Benchmarks with Landscapes Features from GraphFLA
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Department of Computer Science, University of Exeter（埃克塞特大学计算机科学系）
A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI
University of Naples "Federico II" Napoli Italy；Ruhr Institute for Software Technology (paluno), University of Duisburg-Essen Essen Germany；Karlsruhe Institute of Technology (KIT) Karlsruhe Germany；Gran Sasso Science Institute (GSSI) L'Aquila Italy；University of Naples "Federico II"；Ruhr Institute for Software Technology (paluno), University of Duisburg-Essen；Karlsruhe Institute of Technology (KIT)；Gran Sasso Science Institute (GSSI)
TU Dresden（德累斯顿工业大学）；GE Global Research（通用电气全球研究中心）
Microstructure reconstruction is an important and emerging field of research and an essential foundation to improving inverse computational materials engineering (ICME). Much of the recent progress in the field is made based on generative adversarial networks (GANs). Although excellent results have been achieved throughout a variety of materials, challenges remain regarding the interpretability of the model's latent space as well as the applicability to extremely small data sets. The present work addresses these issues by introducing DA-VEGAN, a model with two central innovations. First, a $β$…
Well-trained machine-learning models, which leverage large amounts of open-source software data, have now become an interesting approach to automating many software engineering tasks. Several SE tasks have all been subject to this approach, with performance gradually improving over the past several years with better models and training methods. More, and more diverse, clean, labeled data is better for training; but constructing good-quality datasets is time-consuming and challenging. Ways of augmenting the volume and diversity of clean, labeled data generally have wide applicability. For some …
pyforce-1.0.0: Python Framework for data-driven model Order Reduction of multi-physiCs problEms
Department of Energy, Nuclear Engineering Division, Politecnico di Milano（能源学院，核工程系，米兰理工学院）；Department of Mechanical and Nuclear Engineering and Emirates Nuclear Technology Center, Khalifa University（机械与核工程学院和阿联酋核技术中心，卡比大学）
In this study, a predictive model using Multi-layer Perceptron of Artificial Neural Network architecture was developed to predict customer churn in a financial institution. Previous researches have used supervised machine learning classifiers such as Logistic Regression, Decision Tree, Support Vector Machine, K-Nearest Neighbors, and Random Forest. These classifiers require human effort to perform feature engineering which leads to over-specified and incomplete feature selection. Therefore, this research developed a model to eliminate manual feature engineering in data preprocessing stage. Fif…
The University of Sheffield（谢菲尔德大学）；Indian Institute of Technology, Delhi（印度理工学院德里分校）
Machine learning is a general-purpose technology holding promises for many interdisciplinary research problems. However, significant barriers exist in crossing disciplinary boundaries when most machine learning tools are developed in different areas separately. We present Pykale - a Python library for knowledge-aware machine learning on graphs, images, texts, and videos to enable and accelerate interdisciplinary research. We formulate new green machine learning guidelines based on standard software engineering practices and propose a novel pipeline-based application programming interface (API)…
mtslearn: Machine Learning in Python for Medical Time Series
Nanjing University of Information Science and Technology（南京信息工程大学）；National Institute of Health Data Science, Peking University（北京大学健康医疗大数据国家研究院）；School of Health Humanities, Peking University Health Science Center（北京大学医学人文学院）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学技术研究院）；Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；School of Business English, Sichuan International Studies University（四川外国语大学商务英语学院）；School of Clinical Medicine, Chengdu Medical College（成都医学院临床医学院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；State Key Laboratory of Vascular Homeostasis and Remodeling, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Peking University（北京大学血管稳态与重构全国重点实验室、国家卫生健康委员会心血管分子生物学与调节肽重点实验室）
Purdue University（普渡大学）；Oakland City University（奥克兰城大学）；Institute for Future Intelligence（未来智能研究所）
Computer-aided design (CAD) programs are essential to engineering as they allow for better designs through low-cost iterations. While CAD programs are typically taught to undergraduate students as a job skill, such software can also help students learn engineering concepts. A current limitation of CAD programs (even those that are specifically designed for educational purposes) is that they are not capable of providing automated real-time help to students. To encourage CAD programs to build in assistance to students, we used data generated from students using a free, open source CAD software c…
Warsaw University of Technology（华沙理工大学）；University of Warsaw（华沙大学）
Complex black-box predictive models may have high accuracy, but opacity causes problems like lack of trust, lack of stability, sensitivity to concept drift. On the other hand, interpretable models require more work related to feature engineering, which is very time consuming. Can we train interpretable and accurate models, without timeless feature engineering? In this article, we show a method that uses elastic black-boxes as surrogate models to create a simpler, less opaque, yet still accurate and interpretable glass-box models. New models are created on newly engineered features extracted/le…
