A Meta-Ensemble Classifier Approach: Random Rotation Forest

Ensemble learning is a popular and intensively studied field in machine learning and pattern recognition to increase the performance of the classification. Random forest is very important for giving fast and effective results. on the other hand, Rotation Forest can get better performance than Random Forest. in this study, we present a meta-ensemble classifier, called Random Rotation Forest to utilize and combine the advantages of two classifiers (e.g. Rotation Forest and Random Forest). in the experimental studies, we use three base learners (namely, J48, REPTree, and Random Forest) and two meta-learners (namely, Bagging and Rotation Forest) for ensemble classification on five datasets in UCI Machine Learning Repository. the experimental results indicate that Random Rotation Forest gives promising results according to base learners and bagging ensemble approaches in terms of accuracy rates, AUC, precision, recall, and F-measure values. Our method can be used for image/pattern recognition and machine learning problems

Validity and reliability analysis of the Turkish version of pediatric nutritional risk score scale

BACKGROUND/AIMS: We aimed to perform the validity and reliability analysis of the Turkish version of the Pediatric Nutritional Risk Score (PNRS). MATERIALS AND METHODS: The study group consisted of 149 patients aged between 1 month and 18 years who were admitted to the hospital for at least 48 h. The patients' age, gender, anthropometric measurements, length of stay, admission diagnosis, daily body weights, food consumption, and pain status were recorded. Backward and forward translations into Turkish were done. PNRS was performed by two different physicians. The consistency of the PNRS results was evaluated to determine the validity of PNRS. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. RESULTS: Of all patients, 69 (46.3%) were female and 80 (53.7%) were male. The mean length of the stay was 7.3±4.0 days. The mean age of the patients was 51.9±63.6 months. The Kappa coefficient between the two physicians was 0.66. Weight loss was observed in 65.2% of the patients in the high-risk group and 25.4% in the low-risk group. The hospital malnutrition rate was 31.5%. A higher risk was identified in those with <50% food intake and more severe disease. The specificity, sensitivity, NPV, and PPV of PNRS were 82.1%, 77.8%, 92.0%, and 58.3%, respectively. CONCLUSION: A good consistency suggests that the Turkish validation was achieved successfully. The power of PNRS to discriminate the patients with moderate-low risk of developing malnutrition is higher than the patients with high risk. PNRS is considered a valid and reliable tool to establish the risk of malnutrition in the hospitalized patients

Padovan and Perrin generalized quaternions

In this study, we investigate the Padovan (or Cordonnier) and Perrin generalized quaternions. We obtain the new identities for these special quaternions related to matrix forms. We also introduce Binet-like formulae, generating functions, several summation, and binomial properties concerning these quaternions.WOS:0006591192000012-s2.0-8510737562