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.

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.

Combinatorial interaction testing (CIT) is an effective failure detection method for many types of software systems. This review discusses the current approaches CIT uses in detecting parameter interactions, the difficulties of applying it in practice, recent advances, and opportunities for future research.

Abstract-Sentiment analysis aims to automatically estimate the sentiment in a given text as positive or negative. Polarity lexicons, often used in sentiment analysis, indicate how positive or negative each term in the lexicon is. However, since creating domain-specific polarity lexicons is expensive and timeconsuming, researchers often use a general purpose or domainindependent lexicon. In this work, we address the problem of adapting a general purpose polarity lexicon to a specific domain and propose a simple yet effective adaptation algorithm. We experimented with two sets of reviews from the hotel and movie domains and observed that while our adaptation techniques changed the polarity values for only a small set of words, the overall test accuracy increased significantly: 77% to 83% in the hotel dataset and 61% to 66% in the movie dataset.