Query: feature engineering

title: Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering Based Preprocessing
abstract: For the smart grid energy theft identification, this letter introduces a gradient boosting theft detector (GBTD) based on the three latest gradient boosting classifiers (GBCs) 1) extreme gradient boosting; 2) categorical boosting; and 3) light gradient boosting method. While most of existing machine learning (ML) algorithms just focus on fine tuning the hyperparameters of the classifiers, our ML algorithm, GBTD, focuses on the feature engineering based preprocessing to improve detection performance as well as time complexity. GBTD improves both detection rate and false positive rate (FPR) of those GBCs by generating stochastic features like standard deviation, mean, minimum, and maximum value of daily electricity usage. GBTD also reduces the classifier complexity with weighted feature importance based extraction techniques. Emphasis has been laid upon the practical application of the proposed ML for theft detection by minimizing FPR and reducing data storage space and improving time complexity of the GBTD classifiers. Additionally, this letter proposes an updated version of the existing six theft cases to mimic real world theft patterns and applies them to the dataset for numerical evaluation of the proposed algorithm.

title: Deep learning based feature engineering methods for improved building energy prediction
abstract: The enrichment in building operation data has enabled the development of advanced data driven methods for building energy predictions. Existing studies mainly focused on the utilization of supervised learning techniques for model development, while overlooking the significance of feature engineering. Feature engineering are helpful for reducing data dimensionality, decreasing prediction model complexity, and tackling the problem of corrupted and noisy information. Considering that each building has unique operating characteristics, it is neither practical nor efficient to manually identify features for model developments. Data driven feature engineering methods are thus needed to ensure the flexibility and generalization of building energy prediction models. Using operation data of real buildings, this paper investigates the performance of different deep learning techniques in automatically deriving high quality features for building energy predictions. Three types of deep learning based features are developed using fully connected autoencoders, convolutional autoencoders and generative adversarial networks respectively. Their potentials in building energy predictions have been exploited and compared with conventional feature engineering methods. The study validates the usefulness of deep learning in enhancing building energy prediction performance. The research results help to automate and improve the predictive modeling process while bridging the knowledge gaps between deep learning and building professionals.

title: Deep learning based feature engineering for stock price movement prediction
abstract: Stock price modeling and prediction have been challenging objectives for researchers and speculators because of noisy and non stationary characteristics of samples. With the growth in deep learning, the task of feature learning can be performed more effectively by purposely designed network. In this paper, we propose a novel end to end model named multi filters neural network (MFNN) specifically for feature extraction on financial time series samples and price movement prediction task. Both convolutional and recurrent neurons are integrated to build the multi filters structure, so that the information from different feature spaces and market views can be obtained. We apply our MFNN for extreme market prediction and signal based trading simulation tasks on Chinese stock market index CSI 300. Experimental results show that our network outperforms traditional machine learning models, statistical models, and single structure(convolutional, recurrent, and LSTM) networks in terms of the accuracy, profitability, and stability.

title: A practical feature engineering framework for electricity theft detection in smart grids
abstract: Despite many potential advantages, Advanced Metering Infrastructures have introduced new ways to falsify meter readings and commit electricity theft. This study contributes a new model agnostic, feature engineering framework for theft detection in smart grids. The framework introduces a combination of Finite Mixture Model clustering for customer segmentation and a Genetic Programming algorithm for identifying new features suitable for prediction. Utilizing demand data from more than 4000 households, a Gradient Boosting Machine algorithm is applied within the framework, significantly outperforming the results of prior machine learning, theft detection methods. This study further examines some important practical aspects of deploying theft detection including: the detection delay; the required size of historical demand data; the accuracy in detecting thefts of various types and intensity; detecting irregular and unseen attacks; and the computational complexity of the detection algorithm.

title: Scalable and Unsupervised Feature Engineering Using Vibration Imaging and Deep Learning for Rotor System Diagnosis
abstract: This paper proposes a scalable and unsupervised feature engineering method that uses vibration imaging and deep learning. For scalability, a vibration imaging approach is devised that incorporates data from systems with various scales, such as small testbeds and real field deployed systems. Moreover, a deep learning approach is proposed for unsupervised feature engineering. The overall procedure includes three key steps: 1) vibration image generation; 2) unsupervised feature extraction; and 3) fault classifier design. To demonstrate the validity of the proposed approach, three case studies are conducted using an RK4 rotor kit and a power plant journal bearing system. By incorporating smaller system data as well as real system data, the proposed approach can substantially increase the applicability of the fault diagnosis method while maintaining good accuracy. Moreover, the time and effort needed to develop a diagnostic approach for other rotor systems can be reduced considerably.

title: Paraconsistent Feature Engineering [Lecture Notes]
abstract: Today's modern world is filled with uncertainties and contradictions. As artificial intelligence (AI) advances, machines are frequently expected to mimic the human brain and, consequently, face the conflicts associated with this task. To overcome them, feature engineering has emerged as the field of science responsible for turning raw data into relevant input information, setting up classifiers in the fused digital signal processing (DSP) and pattern recognition (PR) domain. Despite the ongoing efforts to improve feature learning, handcrafted extraction still plays a very important role. In this context, a careful choice of features is extremely relevant for creating an accurate classification. This article sheds light on the problem of feature quality by using a nonclassical logical system capable of handling conflictive situations. It is known as paraconsistent logic (PL)

title: Feature Engineering for Predictive Modeling using Reinforcement Learning
abstract: 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 engineering. It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process significantly influences the cost of model generation. We present a new framework to automate feature engineering. It is based on performance driven exploration of a transformation graph, which systematically and compactly enumerates the space of given options. A highly efficient exploration strategy is derived through reinforcement learning on past examples.

title: Physics Based Feature Engineering
abstract: We describe a new paradigm in computational imaging for performing edge and texture recognition with a superior dynamic range compared to other methods. The algorithm has its origin in Photonic Time Stretch, a realtime instrumentation technology that has enabled observation of ultrafast, non repetitive dynamics and discovery of new scientific phenomena. In this chapter, we introduce the mathematical foundation of this new transform and review its intrinsic properties including the built in equalization property that leads to high performance in visually impaired images. The algorithm is spearheading the development of new methods for feature engineering from visually impaired images with unique and superior properties compared to conventional techniques. It also provides a new approach to the computation of mathematical derivatives via optical dispersion and diffraction. The algorithm is a reconfigurable mathematical operator for hyper dimensional feature detection and signal classification. It has also shown promising results in super resolution single molecule imaging.

title: Feature Engineering for Machine Learning and Data Analytics

title: Expert Feature Engineering vs. Deep Neural Networks: Which Is Better for Sensor Free Affect Detection?
abstract: The past few years have seen a surge of interest in deep neural networks. The wide application of deep learning in other domains such as image classification has driven considerable recent interest and efforts in applying these methods in educational domains. However, there is still limited research comparing the predictive power of the deep learning approach with the traditional feature engineering approach for common student modeling problems such as sensor free affect detection. This paper aims to address this gap by presenting a thorough comparison of several deep neural network approaches with a traditional feature engineering approach in the context of affect and behavior modeling. We built detectors of student affective states and behaviors as middle school students learned science in an open ended learning environment called Betty's Brain, using both approaches. Overall, we observed a tradeoff where the feature engineering models were better when considering a single optimized threshold (for intervention) whereas the deep learning models were better when taking model confidence fully into account (for discovery with models analyses)