Providing an efficient framework for power theft detection based on combination of Raven roosting optimization algorithm and clustering and classification techniques

One of the main concerns of power generation systems around the world is electricity theft. One of the goals of the Advanced Measurement Infrastructure (AMI) is to reduce the risk of electricity theft in the electric smart grids. However, the use of smart meters and the addition of a security layer to the measurement system paved the way for electricity theft. Nowadays, machine learning and data mining technologies are used to find abnormal patterns of consumption. The lack of a comprehensive dataset about abnormal consumption patterns, the issue of choosing effective features, the balance between consumer\u27s normal and abnormal consumption patterns, and the choice of type and number of classifiers and how to combine them are the challenges of these technologies. Therefore, a detection system for electricity theft that is capable of effectively detecting theft attacks is needed. To this end, a framework including data preparation phases, feature selection, clustering, and combined modeling have been proposed to address the aforementioned challenges. In order to balance normal and abnormal data, 6 artificial attacks have been created. Moreover, with respect to the Chief element in the Raven optimization algorithm and its two-step search feature, this algorithm has been used in feature selection and clustering phases. Stacking as a two-step combined modeler has been used to strengthen the prediction of accuracy. In the second step of this modeler, the meta-Gaussian Processes algorithm is used due to the high accuracy of detection. The Irish Social Science Data Archive (ISSDA) dataset has been used to evaluate performance. The results show that the proposed method identifies dishonest customers with higher accurac

The Automatic Statistician: A Relational Perspective

Department of Computer EngineeringGaussian Processes (GPs) provide a general and analytically tractable way of capturing complex time-varying, nonparametric functions. The time varying parameters of GPs can be explained as a composition of base kernels such as linear, smoothness or periodicity in that covariance kernels are closed under addition and multiplication. The Automatic Bayesian Covariance Discovery (ABCD) system constructs natural-language description of time-series data by treating unknown time-series data nonparametrically using GPs. Unfortunately, learning a composite covariance kernel with a single time-series dataset often results in less informative kernels instead of finding qualitative distinct descriptions. We address this issue by proposing a relational kernel learning which can model relationship between sets of data and find shared structure among the time series datasets. We show the shared structure can help learning more accurate models for sets of regression problems with some synthetic data, US top market capitalization stock data and US house sales index data.ope

Lifted graphical models: a survey

Lifted graphical models provide a language for expressing dependencies between different types of entities, their attributes, and their diverse relations, as well as techniques for probabilistic reasoning in such multi-relational domains. In this survey, we review a general form for a lifted graphical model, a par-factor graph, and show how a number of existing statistical relational representations map to this formalism. We discuss inference algorithms, including lifted inference algorithms, that efficiently compute the answers to probabilistic queries over such models. We also review work in learning lifted graphical models from data. There is a growing need for statistical relational models (whether they go by that name or another), as we are inundated with data which is a mix of structured and unstructured, with entities and relations extracted in a noisy manner from text, and with the need to reason effectively with this data. We hope that this synthesis of ideas from many different research groups will provide an accessible starting point for new researchers in this expanding field

Fast Construction of Relational Features for Machine Learning

Katedra kybernetik

Multi-Relational Learning with Gaussian Processes

Due to their flexible nonparametric nature, Gaussian process models are very effective at solving hard machine learning problems. While existing Gaussian process models focus on modeling one single relation, we present a generalized GP model, named multi-relational Gaussian process model, that is able to deal with an arbitrary number of relations in a domain of interest. The proposed model is analyzed in the context of bipartite, directed, and undirected univariate relations. Experimental results on real-world datasets show that exploiting the correlations among different entity types and relations can indeed improve prediction performance.