29 papers · ranked by Valyu relevance
Jeff Heaton
—Machine learning models, such as neural networks, decision trees, random forests and gradient boosting machines accept a feature vector and provide a prediction. These models learn in a supervised fashion where a set of feature vectors with expected output is provided. It is very common practice to engineer new…
Davide Chicco, Luca Oneto, Erica Tavazzi, Francis Ouellette
Applying computational statistics or machine learning methods to data is a key component of many scientific studies, in any field, but alone might not be sufficient to generate robust and reliable outcomes and results. Before applying any discovery method, preprocessing steps are necessary to prepare the data to the…
Angelos Chatzimparmpas, Rafael M. Martins, Kostiantyn Kucher, Andreas Kerren
'Andreas Kerren'] Abstract—The machine learning (ML) life cycle involves a series of iterative steps, from the effective gathering and preparation of the data—including complex feature engineering processes—to the presentation and improvement of results, with various algorithms to choose from in every step. Feature…
Udayan Khurana, Horst Samulowitz, Deepak S. Turaga
Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effective feature…
Yihe Dong, Sercan Ö. Arık, Nathanael C. Yoder, Tomas Pfister
Feature engineering has demonstrated substantial utility for many machine learning workflows, such as in the small data regime or when distribution shifts are severe. Thus automating this capability can relieve much manual effort and improve model performance. Towards this, we propose AutoMAN, or Automated Mask-based…
Sammuel Ramos Silva, Rodrigo Rocha Silva
Automated Feature Engineering (AutoFE) has become an important task for any machine learning project, as it can help improve model performance and gain more information for statistical analysis. However, most current approaches for AutoFE rely on manual feature creation or use methods that can generate a large number…
Qitao Shi, Yalin Zhang, Longfei Li, Xinxing Yang + 2 more
'Jun Zhou'] Abstract—Machine learning techniques have been widely applied in Internet companies for various tasks, acting as an essential driving force, and feature engineering has been generally recognized as a crucial tache when constructing machine learning systems. Recently, a growing effort has been made to the…
Konstantinos Sikelis, George E. Tsekouras, Konstantinos Kotis
The Semantic Web emerged as an extension to the traditional Web, towards adding meaning to a distributed Web of structured and linked data. At its core, the concept of ontology provides the means to semantically describe and structure information and data and expose it to software and human agents in a machine and…
Syed Ubaid Qurashi
We present Topology-Driven Directed Flow (TDDF), a practical feature engineering method for biological classification in low-data regimes. Inspired by the metaphor of water flowing through a straw leaning toward a fixed destination, TDDFextracts three interpretable features from high-dimensional biological space (1)…
Dongjie Wang, Yanyong Huang, Wangyang Ying, Haoyue Bai + 10 more
Reinforcement, and Generative Approaches for Tabular Data Transformation Authors: ['Dongjie Wang' 'Yanyong Huang' 'Wangyang Ying' 'Haoyue Bai' 'Nanxu Gong' 'Xinyuan Wang' 'Sixun Dong' 'Zhe Tao' 'Kunpeng Liu' 'Meng Xiao' 'Pengfei Wang' 'Pengyang Wang' 'Hui Xiong' 'Yanjie Fu'] DONGJIE WANG, University of Kansas, United…
Şafak Kılıç, Asadullah Shaikh
Following the training of the ResNet50+CBAM architecture, a structured deep feature engineering (DFE) approach is introduced to further enhance classification performance and generalizability. This pipeline integrates multi-layer deep feature extraction, advanced feature selection, and shallow learning-based…
Mriganka Roy, Olga Wodo, Maria Magdalena Pastor
Surrogate models (SM) serve as a proxy to the physics- and experiment-based models to significantly lower the cost of prediction while providing high accuracy. Building an SM for additive manufacturing (AM) process suffers from high dimensionality of inputs when part geometry or tool-path is considered in addition to…
Hardik Prabhu, Hrushikesh Bhosale, Aamod Sane, Renu Dhadwal + 2 more
'Vigneshwar Ramakrishnan' 'Jayaraman Valadi'] AMPylation is a biologically significant yet understudied post-translational modification where an adenosine monophosphate (AMP) group is added to Tyrosine and Threonine residues primarily. While recent work has illuminated the prevalence and functional impacts of…
Kenneth D. Roe, Vibhu Jawa, Xiaohan Zhang, Christopher G. Chute + 5 more
'Jeremy A. Epstein' 'Jordan Matelsky' 'Ilya Shpitser' 'Casey Overby Taylor' 'Ozlem Uzuner'] Incorporating expert knowledge at the time machine learning models are trained holds promise for producing models that are easier to interpret. The main objectives of this study were to use a feature engineering approach to…
Shiri Baum, Ido Meshulam, Yadid M Algavi, Omri Peleg + 1 more
TAGINE is a feature engineering algorithm that leverages the microbial taxonomic tree to optimize feature sets in microbiome data for predictive modeling. The algorithm starts with features at high taxonomic levels and iteratively splits them into lower-level clades in cases where it improves predictive accuracy…
Christoph Bienefeld, Florian Michael Becker-Dombrowsky, Etnik Shatri, Eckhard Kirchner + 2 more
'Eckhard Kirchner' 'Claude Delpha' 'Demba Diallo'] The engineering challenge of rolling bearing condition monitoring has led to a large number of method developments over the past few years. Most commonly, vibration measurement data are used for fault diagnosis using machine learning algorithms. In current research…
Jose Cleydson F. Silva, Layla Schuster, Nick Sexson, Melissa Erdem + 4 more
Characterizing protein families’ structural and functional diversity is essential for understanding their biological roles. Traditional analyses often focus on primary and secondary structures, which may not fully capture complex protein interactions. Here we introduce InteracTor, a novel toolkit that extracts…
Ruben Ruiz-Gonzalez, Jaime Gomez-Gil, Francisco Javier Gomez-Gil, Víctor Martínez-Martínez
'Víctor Martínez-Martínez'] The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is…
Authors not listed
The accurate prediction of fuel mixture properties is essential for the development of alternative fuels, yet remains challenging under data-scarce conditions due to the combinatorial complexity of multi-component systems. In this study, we present a systematic evaluation of three machine learning (ML)…
Hashim Ali, Raja Sarath Kumar Boddu, Umer Tanveer, Aamir Saeed + 4 more
Software requirements classification remains one of the important challenges in requirements engineering. Engineering that affects the smoothness of project success about software development life cycles. in this paper, a novel hybrid solution is being presented that beats the benchmarks set by previous approaches…
Thomas P. Quinn, Samuel C. Lee, Svetha Venkatesh, Thin Nguyen
Although neuropsychiatric disorders have a well-established genetic background, their specific molecular foundations remain elusive. This has prompted many investigators to design studies that identify explanatory biomarkers, and then use these biomarkers to predict clinical outcomes. One approach involves using…
Tianxun Zhou, Calvin Chee Hoe Cheah, Eunice Wei Mun Chin, Jie Chen + 3 more
In recent years, supervised machine learning models trained on videos of animals with pose estimation data and behavior labels have been used for automated behavior classification. Applications include, for example, automated detection of neurological diseases in animal models. However, there are two problems with…
Daniel Bojar
Next to being targeted by most available drugs, human proteins located in the plasma membrane are also responsible for a plethora of essential cellular functions, ranging from signaling to transport processes. In order to target and study these transmembrane proteins, their plasma membrane location has to be…
Hasan M. Sayeed, Sterling G. Baird, Taylor D. Sparks
Capturing structure-property relationships of materials for property prediction using machine learning requires the representation or featurization of the structural aspects of materials at different levels, including atomic, crystal, and microscales. While crystal structure-based modeling techniques are effective for…
Authors not listed
Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource consumption and enhancing the likelihood of clinical success via…
Suvo Banik, Karthik Balasubramanian, Sukriti Manna, Sybil Derrible + 1 more
Identifying key descriptors and understanding important features across different classes of materials are crucial for machine learning (ML) tools to both predict material properties and reveal the physics underlying any process of interest. Traditionally, the predictive modeling of elastic properties of materials is…
Shang Zhu, Bichlien H. Nguyen, Yingce Xia, Kali Frost + 3 more
Rapid prediction of environmental chemistry properties is critical towards the green and sustainable development of chemical industry and drug discovery. Machine learning methods can be applied to learn the relations between chemical structures and their environmental impact. Graph machine learning, by learning the…
Authors not listed
Deciphering the correct mechanism governing certain phenomenon in polyelectrolyte (PE) brush grafted systems, revealed through atomistic simulations, is an extremely challenging problem. In a recent study, our all-atom molecular dynamics (MD) simulations revealed a non-linearly large electroosmotic flow (in the…
Authors not listed
Monoterpene synthases (mTSs) are a large family of enzymes, which have promising industrial applications, yet remain difficult to engineer due to complex and poorly understood sequence-function relationships. Here, we present a structure-based machine learning (ML) framework that accurately predicts whether a mTS…