Brain image clustering by wavelet energy and CBSSO optimization algorithm

Previously, the diagnosis of brain abnormality was significantly important in the saving of social and hospital resources. Wavelet energy is known as an effective feature detection which has great efficiency in different utilities. This paper suggests a new method based on wavelet energy to automatically classify magnetic resonance imaging (MRI) brain images into two groups (normal and abnormal), utilizing support vector machine (SVM) classification based on chaotic binary shark smell optimization (CBSSO) to optimize the SVM weights. The results of the suggested CBSSO-based KSVM are compared favorably to several other methods in terms of better sensitivity and authenticity. The proposed CAD system can additionally be utilized to categorize the images with various pathological conditions, types, and illness modes

Estimating Probabilities of Default With Support Vector Machines

This paper proposes a rating methodology that is based on a non-linear classification method, the support vector machine, and a non-parametric technique for mapping rating scores into probabilities of default. We give an introduction to underlying statistical models and represent the results of testing our approach on German Bundesbank data. In particular we discuss the selection of variables and give a comparison with more traditional approaches such as discriminant analysis and the logit regression. The results demonstrate that the SVM has clear advantages over these methods for all variables tested.Bankruptcy, Company rating, Default probability, Support vector machines.

Estimating probabilities of default with support vector machines

This paper proposes a rating methodology that is based on a non-linear classification method, the support vector machine, and a non-parametric technique for mapping rating scores into probabilities of default. We give an introduction to underlying statistical models and represent the results of testing our approach on Deutsche Bundesbank data. In particular we discuss the selection of variables and give a comparison with more traditional approaches such as discriminant analysis and the logit regression. The results demonstrate that the SVM has clear advantages over these methods for all variables tested. --Bankruptcy,Company rating,Default probability,Support vector machines

Support Vector Machines for Credit Scoring and discovery of significant features

The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit scoring for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines against these traditional methods on a large credit card database. We find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default. 1

Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection

This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recastaccuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian radial basis function kernel outperforms the rest of the models for the longest forecast horizons. We also find that machine learning methods improve their forecasting accuracy with respect to linear models as forecast horizons increase. This results shows the suitability of SVR for medium and long term forecasting.Peer ReviewedPostprint (published version

Biosignals as an Advanced Man-Machine Interface

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such an MMI requires the correct classification of biosignals to emotion classes. This paper explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 24 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for both personalized biosignal-profiles and the recording of multiple biosignals in parallel

Automatic text categorisation of racist webpages

Automatic Text Categorisation (TC) involves the assignment of one or more predefined categories to text documents in order that they can be effectively managed. In this thesis we examine the possibility of applying automatic text categorisation to the problem of categorising texts (web pages) based on whether or not they are racist. TC has proven successful for topic-based problems such as news story categorisation. However, the problem of detecting racism is dissimilar to topic-based problems in that lexical items present in racist documents can also appear in anti-racist documents or indeed potentially any document. The mere presence of a potentially racist term does not necessarily mean the document is racist. The difficulty is finding what discerns racist documents from non-racist. We use a machine learning method called Support Vector Machines (SVM) to automatically learn features of racism in order to be capable of making a decision about the target class of unseen documents. We examine various representations within an SVM so as to identify the most effective method for handling this problem. Our work shows that it is possible to develop automatic categorisation of web pages, based on these approache

Estimating Probabilities of Default With Support Vector Machines

This paper proposes a rating methodology that is based on a non-linear classification method, the support vector machine, and a non-parametric technique for mapping rating scores into probabilities of default. We give an introduction to underlying statistical models and represent the results of testing our approach on German Bundesbank data. In particular we discuss the selection of variables and give a comparison with more traditional approaches such as discriminant analysis and the logit regression. The results demonstrate that the SVM has clear advantages over these methods for all variables tested

Text categorization with support vector machines

This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies, why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and they behave robustly over a variety of different learning tasks. Furthermore, they are fully automatic, eliminating the need for manual parameter tuning. The paper is written in English