ProceedingsoftheTwenty-SixthInternationalJointConferenceonArticialIntelligence(IJCAI-17) Learning Feature Engineering for Classication 1 2 2 FatemehNargesian , Horst Samulowitz , Udayan Khurana 3 2 Elias B. Khalil , Deepak Turaga 1University of Toronto, 2IBM Research, 3Georgia Institute of Technology fnargesian@cs.toronto.edu, {samulowitz, ukhurana}@us.ibm.com, lyes@gatech.edu, turaga@us.ibm.com Abstract search in feature space using heuristic feature quality mea- Feature engineering is the task of improving pre- sures (such as information gain) and other surrogate mea- [ dictive modelling performance on a dataset by sures of performance ...
Feature Engineering in Machine Learning Zdenek Zabokrtsk´y Institute of Formal and Applied Linguistics, Charles University in Prague Used resources http://www.cs.princeton.edu/courses/archive/spring10/cos424/slides/18-feat.pdf http://stackoverow.com/questions/2674430/how-to-engineer-features-for-machine-learning https://facwiki.cs.byu.edu/cs479/index.php/Feature engineering documentation of scikit-learn wikipedia Human’s role when applying Machine Learning Machine learning provides you with extremely powerful tools for decision making ... ... but until a breakthrough in AI, the role of the developer’s decision will still be crucial. Your responsibility: setting up the correct problem to ...
AutoML Feature Engineering for Student Modeling yields High Accuracy, but Limited Interpretability Nigel Bosch University of Illinois Urbana-Champaign pnb@illinois.edu Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The methods we compare, Featuretools and TSFRESH (Time Series FeatuRe Extraction on basis of Scalable ...
BathEMADE: Evolutionary Multi- objective Algorithm Design Engine for Bathymetric LIDAR Jason Zutty Rodd Talebi James Rick Christopher Valenta Domenic Carr 1 Gregory Rohling 2 How is a Machine Learning Algorithm Made? • Involves a number of steps Data Feature Model Model Parameter Model Raw Data Preparation Engineering Selection & Evaluation Optimization Deployment Training 2 3 How is a Machine Learning Algorithm Made? • Involves a number of steps Data Feature Model Model Parameter Model Raw Data Preparation Engineering Selection ...
Neural Feature Search: A Neural Architecture for Automated Feature Engineering 1, 2, , 2, 3 4 Xiangning Chen *, Qingwei Lin * **, Chuan Luo *, Xudong Li , Hongyu Zhang , 2 5 2 6 2 Yong Xu , Yingnong Dang , Kaixin Sui , Xu Zhang , Bo Qiao , 2 7 5 2 Weiyi Zhang , Wei Wu , Murali Chintalapati and Dongmei Zhang 1Tsinghua University, China 2Microsoft Research, China 3University of California, Los Angeles, United States 4The University of Newcastle, Australia 5Microsoft Azure, United States 6Nanjing ...
Automated Feature Engineering for Deep Neural Networks with Genetic Programming by Jeff Heaton An idea paper submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science College of Engineering and Computing Nova Southeastern University April 2016 2 Abstract Feature engineering is a process that augments the feature vector of a predictive model with calculated values that are designed to enhance the model’s performance. Models such as neural networks, support vector machines and ...
Cognizant 20-20 Insights Digital Business Accelerating Machine Learning as a Service with Automated Feature Engineering Building scalable machine learning as a service, or MLaaS, is critical to enterprise success. Key to translate machine learning project success into program success is to solve the evolving convoluted data engineering challenge, using local and global data. Enabling sharing of data features across a multitude of models within and across various line of business is pivotal to program success. Executive Summary 1 The success ...
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