In this paper a novel architecture of Support Vector Machine classifiers utilizing binary decision tree (SVM-BDT) for solving multiclass problems is presented. The hierarchy of binary decision subtasks using SVMs is designed with a clustering algorithm. For consistency between the clustering model and SVM, the clustering model utilizes distance measures at the kernel space, rather than at the input space. The proposed SVM based Binary Decision Tree architecture takes advantage of both the efficient computation of the decision tree architecture and the high classification accuracy of SVMs. The SVMBDT architecture was designed to provide superior multi-class classification performance. Its performance was measured on samples from MNIST, Pendigit, Optdigit and Statlog databases of handwritten digits and letters. The results of the experiments indicate that while maintaining comparable or offering better accuracy with other SVM based approaches, ensembles of trees (Bagging and Random Forest) and neural network, the training phase of SVM-BDT is faster. During recognition phase, due to its logarithmic complexity, SVM-BDT is much faster than the widely used multi-class SVM methods like “one-against-one” and “one-against-all”, for multiclass problems. Furthermore, the experiments showed that the proposed method becomes more favourable as the number of classes in the recognition problem increases. Povzetek: Predstavljena je metoda gradnje binarnih dreves z uporabo SVM za vecrazredne probleme.

/pdf/a-multi-class-svm-classifier-utilizing-binary-decision-tree-36ptmwoxdt.pdf

A Multi-class SVM Classifier Utilizing Binary Decision Tree

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future.

/pdf/the-challenge-of-non-technical-loss-detection-using-526r5ay01j.pdf

The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey

In this paper, we fill a gap in the literature by studying the problem of Arabic handwritten digit recognition The performances of different classification and feature extraction techniques on recognizing Arabic digits are going to be reported to serve as a benchmark for future work on the problem The performance of well known classifiers and feature extraction techniques will be reported in addition to a novel feature extraction technique we present in this paper that gives a high accuracy and competes with the state-of-the-art techniques A total of 54 different classifier/features combinations will be evaluated on Arabic digits in terms of accuracy and classification time The results are analyzed and the problem of the digit ‘0’ is identified with a proposed method to solve it Moreover, we propose a strategy to select and design an optimal two-stage system out of our study and, hence, we suggest a fast two-stage classification system for Arabic digits which achieves as high accuracy as the highest classifier/features combination but with much less recognition time

Arabic handwritten digit recognition

Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.

/pdf/large-scale-detection-of-non-technical-losses-in-imbalanced-4cubbz5p9n.pdf

Large-scale detection of non-technical losses in imbalanced data sets

This work proposes an efficient three-stage classifier for handwritten digit recognition based on NN (neural network) and SVM (support vector machine) classifiers. The classification is performed by 2 NNs and one SVM. The first NN is designed to provide a low misclassification rate using a strong rejection criterion. It is applied on a small set of easy to extract features. Rejected patterns are forwarded to the second NN that uses additional, more complex features, and utilizes a well-balanced rejection criterion. Finally, rejected patterns from the second NN are forwarded to an optimized SVM that considers only the "top k" classes as ranked by the NN. This way a very fast SVM classification is obtained without sacrificing the classifier accuracy. The obtained recognition rate is among the best on the MNIST database and the classification time is much better compared to the single SVM applied on the same feature set.

http://dejan.gjorgjevikj.com/papers/icpr2004_gorgevikd.pdf

An efficient three-stage classifier for handwritten digit recognition

Recent results in pattern recognition have shown that SVM (support vector machine) classifiers often have superior recognition rates in comparison to other classification methods In this paper, a cooperation of four SVM classifiers for handwritten digit recognition, each using different feature set is examined We investigate the advantages and weaknesses of various cooperation schemes based on classifier decision fusion using statistical reasoning The obtained results show that it is difficult to exceed the recognition rate of a single, well-tuned SVM classifier applied straightforwardly on all feature sets In our experiments only one of the cooperation schemes exceeds the recognition rate of a single SVM classifier However, the classifier cooperation reduces the classifier complexity and need for training samples, decreases classifier training time and sometimes improves the classifier performance

Handwritten Digit Recognition by Combining SVM Classifiers

In this paper, the cooperation of two feature families for handwritten digit recognition using SVM (support vector machine) classifiers is examined. We investigate the advantages and weaknesses of various decision fusion schemes using statistical and rule-based reasoning. The obtained results show that it is difficult to exceed the recognition rate of a single classifier applied straightforwardly on both feature families as one set by rule based reasoning applied on the individual classifier decisions. However, the rule-based cooperation schemes enable an easy and efficient implementation of various rejection criteria. On the other hand, the statistical cooperation schemes offer a better possibility for fine tuning of the recognition versus the reliability tradeoff, which leads to recognition systems with high reliability that also keep high recognition rates.

/pdf/handwritten-digit-recognition-using-statistical-and-rule-1t71ycto3n.pdf

D. Gorgevik

Papers

An efficient three-stage classifier for handwritten digit recognition

Handwritten Digit Recognition by Combining SVM Classifiers

Handwritten digit recognition using statistical and rule-based decision fusion