You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey 
Chapter Title:  E. Other methods 
1. Abstract of Cited Paper (BIB001):  Fraud detection in energy systems by illegal con- sumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as Artificial Neural Networks and Support Vector Machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the Optimum-Path Forest classifier for a fast non-technical losses recognition, which has been demonstrated to be superior than neural networks and similar to Support Vector Machines, but much faster. Comparisons among these classifiers are also presented. 
2. Abstract of Cited Paper (BIB002):  Nowadays the electric utilities have to handle problems with the non-technical losses caused by frauds and thefts committed by some of their consumers. In order to minimize this, some methodologies have been created to perform the detection of consumers that might be fraudsters. In this context, the use of classification techniques can improve the hit rate of the fraud detection and increase the financial income. This paper proposes the use of the knowledge-discovery in databases process based on artificial neural networks applied to the classifying process of consumers to be inspected. An experiment performed in a Brazilian electric power distribution company indicated an improvement of over 50% of the proposed approach if compared to the previous methods used by that company. 
3. Abstract of Cited Paper (BIB003):  Abstract High values of total percentage losses of electricity in Serbia’s distribution network and a very small percentage of success in detecting electricity fraud indicate a need for a sophisticated means of detecting fraud perpetrators. In order for this task to be performed efficiently, the authors of the paper emphasize the need for a suitable and comprehensive use of the billing system or the database of the amounts of invoiced spent electricity in accounting periods and other relevant data regarding registered customers. For that purpose, they suggest using the rough set theory and give a general approach to its use. The point of the paper is forming a criterion for the estimation of accurate (suitable) discretization of original data. The criterion is based on the amount of lost not invoiced electricity due to electricity fraud. Based on consumption characteristics of detected fraud of customers whose measurement points were regularly (monthly) read, a list of the suspicious customers will be formed which will serve as the basis for sending expert teams to specified locations with the task to confirm or dismiss the fraud suspicion. 
4. Abstract of Cited Paper (BIB004):  Electricity theft is a major concern for the utilities. With the advent of smart meters, the frequency of collecting household energy consumption data has increased, making it possible for advanced data analysis, which was not possible earlier. We have proposed a temperature dependent predictive model which uses smart meter data and data from distribution transformer to detect electricity theft in an area. The model was tested for varying amounts of power thefts and also for different types of circuit approximations. The results are encouraging and the model can be used for real world application.

Literature Review:
Optimum path forests (OPF), a graph-based classifier, is used in  . It builds a graph in the feature space and uses socalled "prototypes" or training samples. Those become roots of their optimum-path tree node. Each graph node is classified based on its most strongly connected prototype. This approach is fundamentally different to most other learning algorithms such as SVMs or neural networks which learn hyperplanes. Optimum path forests do not learn parameters, thus making training faster, but predicting slower compared to parametric methods. They are used in BIB002 for 736 customers and achieved a test accuracy of 0.9021, outperforming SMVs with Gaussian and linear kernels and a neural network which achieved test accuracies of 0.8893, 0.4540 and 0.5301, respectively. Related results and differences between these classifiers are reported in  . Rough sets give lower and upper approximations of an original conventional or crisp set. Rough set analysis is applied to NTL detection in BIB004 on features related to BIB003 . This supervised learning technique allows to approximate concepts that describe fraud and regular use. A test accuracy of 0.9322 is reported. The first application of rough set analysis applied to NTL detection is described in  on 40K customers, but lacks details on the attributes used per customer, for which a test accuracy of 0.2 is achieved. Different feature selection techniques for customer master data and consumption data are assessed in BIB001 . Those methods include complete search, best-first search, genetic search and greedy search algorithms for the master data. Other features called shape factors are derived from the consumption time series including the impact of lunch times, nights and weekends on the consumption. These features are used in K-means for clustering similar consumption time series. In the classification step, a decision tree is used to predict whether a customer causes NTLs or not. An overall test accuracy of 0.9997 is reported. A different method is to estimate NTLs by subtracting an estimate of the technical losses from the overall losses BIB005 . It models the resistance of the infrastructure in a temperaturedependent model using regression which approximates the technical losses. It applies the model to a database of 30 customers for which the consumption was recorded for six days with meter readings every 30 minutes for theft levels of 1, 2, 3, 4, 6, 8 and 10%. The respective test recalls in linear circuits are 0.2211, 0.7789, 0.9789, 1, 1, 1 and 1, respectively.