A new classification algorithm, called VFI5 (for Voting Feature Intervals), is developed and applied to problem of differential diagnosis of erythemato-squamous diseases. The domain contains records of patients with known diagnosis. Given a training set of such records, the VFI5 classifier learns how to differentiate a new case in the domain. VFI5 represents a concept in the form of feature intervals on each feature dimension separately. classification in the VFI5 algorithm is based on a real-valued voting. Each feature equally participates in the voting process and the class that receives the maximum amount of votes is declared to be the predicted class. The performance of the VFI5 classifier is evaluated empirically in terms of classification accuracy and running time.

A n e w machine learning algorithm f o r the diagnosis of cardiac arrhythmia f r o m standard 12 lead ECG recordings i s presented. T h e algorithm i s called VFI5 f o r Voting Feature Intervals. VFI5 i s a supervised and inductive learning algorithm for inducing classification knowledge f r o m examples. T h e input t o VFIS i s a traini n g set IiitroductioiiIn several iiiedical domains the machine learning algorithiiis were actually applied, for example, two classificatioii algorithnis are used in localization of primary tumor? prognostics of recurrence of breast cancer, diagnosis of thyroid diseases, and rheumatology [4]. Another example is the CRLS system applied t o a biomedical domain [5]. This paper presents a new machine learning algorit,lim for another medical problem, which is the of cardiac arrhytliinia from standard 12 lead E N ; recordings.

One of the application areas of genetic algorithms is parameter optimization. This paper addresses the problem of optimizing a set of parameters that represent the weights of criteria, where the sum of all weights is 1. A chromosome represents the values of the weights, possibly along with some cut-off points. A new crossover operation, called continuous uniform crossover, is proposed, such that it produces valid chromosomes given that the parent chromosomes are valid. The new crossover technique is applied to the problem of multicriteria inventory classification. The results are compared with the classical inventory classification technique using the Analytical Hierarchy Process.

A new classification algorithm called VFI (for Voting Feature Intervals) is proposed. A concept is represented by a set of feature intervals on each feature dimension separately. Each feature participates in the classification by distributing real-valued votes among classes. The class receiving the highest vote is declared to be the predicted class. VFI is compared with the Naive Bayesian Classifier, which also considers each feature separately. Experiments on real-world datasets show that VFI achieves comparably and even better than NBC in terms of classification accuracy. Moreover, VFI is faster than NBC on all datasets.

Abstract.A m e c hanism for learning lexical correspondences between two languages from sets of translated sentence pairs is presented. These lexical level correspondences are learned using analogical reasoning between two translation examples. Given two translation examples, the similar parts of the sentences in the source language must correspond to the similar parts of the sentences in the target language. Similarly, the di erent parts must correspond to the respective parts in the translated sentences. The correspondences between similarities and between di erences are learned in the form of translation templates. A translation template is a generalized translation exemplar pair where some components are generalized by replacing them with variables in both sentences and establishing bindings between these variables. The learned translation templates are obtained by replacing di erences or similarities by v ariables. This approach has been implemented and tested on a set of sample training datasets and produced promising results for further investigation.

This paper presents an expert system for differential diagnosis of erythemato-squamous diseases incorporating decisions made by three classification algorithms: nearest neighbor classifier, naive Bayesian classifier and voting feature intervals-5. This tool enables doctors to differentiate six types of erythemato-squamous diseases using clinical and histopathological parameters obtained from a patient. The program also gives explanations for the classifications of each classifier. The patient records are also maintained in a database for further references. ᭧

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learned by the RIMARC algorithm can be used for accurately classifying the preoperative rhythm status. APs were included from 221 SR and 158 AF patients. During a learning phase, the RIMARC algorithm established a ranking order of 62 features by predictive value for SR or AF. The model was then challenged with an additional test set of features from 28 patients in whom rhythm status was blinded. The accuracy of the risk prediction for AF by the model was very good (0.93) when all features were used. Without the seven AP features, accuracy still reached 0.71. In conclusion, we have shown that training the machine-learning algorithm RIMARC with an experimental and clinical data set allows predicting a classification in a test data set with high accuracy. In a clinical setting, this approach may prove useful for finding hypothesis-generating associations between different parameters.Abstract Ex vivo recorded action potentials (APs) in human right atrial tissue from patients in sinus rhythm (SR) or atrial fibrillation (AF) display a characteristic spike-anddome or triangular shape, respectively, but variability is huge within each rhythm group. The aim of our study was to apply the machine-learning algorithm ranking instances by maximizing the area under the ROC curve (RIMARC) to a large data set of 480 APs combined with retrospectively collected general clinical parameters and to test whether the rules Ursula Ravens, Deniz Katircioglu-Öztürk, Ali Oto and H. Altay Güvenir have equally contributed. Action potential duration at 20 % of repolarization (ms) APD 50 Action potential duration at 50 % of repolarization (ms) APD 90 Action potential duration at 90 % of repolarization (ms) dV/dt max Maximum rate of depolarization (Vs −1 ) MAD Maximum area under ROC curve-based discretization PLT 20 "Plateau potential" defined as the mean potential (mV) in the time window between 20 % of APD 90 plus 5 ms RIMARC Ranking instances by maximizing the area under the ROC curve RMP Resting membrane potential (mV) ROC Receiver operating characteristics SR Sinus rhythm Electronic supplementary material