Statistical Data Modeling and Machine Learning with Applications

The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties

MARS ve BRT Veri Madenciliği Yöntemlerinin Sınıflama Performanslarının Karşılaştırılması: ABİDE- 2016 Örneği

This research examined the relationships between student, teacher, school and instructional qualifications and 8th grade students' science achievement, based on the conceptual framework created by Nilsen and Gustafsson (2016), using data mining methods MARS and BRT. Research data (n=10407 students, n=941 teachers and n=865 school administrators) were obtained from the ABİDE study conducted at the national level by the Ministry of National Education in 2016. MARS and BRT analyzes were performed in the SPM 8.2 program. The science achievement classification performances of these methods were compared by considering the correct classification rate, sensitivity and specificity rates, F1 statistical value and the area under the ROC curve. It was found that the BRT method was more successful than the MARS method in terms of all these criteria, and the most important predictors of science achievement were similar compared to these two methods. The results revealed that the most important predictors of science success are the student's perception of science self-efficacy, the father's occupation, the family's monthly income, the instructional activities of the teacher, the teacher's experience and preparation for the lesson, and the school administrators' perception of school climate. It is thought that the reason why BRT outperforms the MARS method in terms of the criteria considered in this study is that BRT learns from errors with the additive combination of various regression trees and provides a stronger classification performance by minimizing the errors that may occur in classification. This study revealed the benefits of using these two data mining methods in the field of Educational Sciences and discussed the contribution of the related methods in this field.Bu araştırmada öğrenci, öğretmen, okul ve öğretimsel nitelikler ile 8. Sınıf öğrencilerinin fen başarısı arasındaki ilişkiler, Nilsen ve Gustafsson’ın (2016) oluşturdukları kavramsal çerçeve temel alınarak veri madenciliği yöntemlerinden olan MARS ve BRT ile incelenmiştir. Araştırma verileri (n=10407 öğrenci, n=941 öğretmen ve n=865 okul yöneticisi), 2016 yılında Milli Eğitim Bakanlığı tarafından ulusal düzeyde gerçekleştirilen ABİDE çalışmasından elde edilmiştir. MARS ve BRT analizleri SPM 8.2 programında gerçekleştirilmiş ve bu yöntemlerin fen başarısını sınıflandırma performansları; doğru sınıflandırma oranı, duyarlılık ve özgüllük oranları ile F1 istatistik değeri ve ROC eğrisi altında kalan alan dikkate alınarak karşılaştırılmıştır. Bu doğrultuda tüm bu ölçütler açısından BRT yönteminin MARS yöntemine göre daha başarılı olduğu ve fen başarısının en önemli yordayıcılarının da bu iki yönteme göre benzer olduğu bulunmuştur. Araştırma sonuçları fen başarısının en önemli yordayıcılarının öğrencin fene ilişkin özyeterlik algısı, baba mesleği, ailenin aylık geliri, öğretmenin öğretimsel etkinlikleri, öğretmenin deneyimi ve derse hazırlığı ile okul yöneticilerinin okul iklim algısı olduğunu ortaya koymuştur. Bu çalışmada dikkate alınan ölçütler açısından BRT’nin MARS yöntemine göre daha iyi bir performans sergilemesinin nedeninin, BRT'nin çeşitli regresyon ağaçlarının toplamsal birleşimi ile hatalardan öğrenmesi ve sınıflandırmada oluşabilecek hataları en aza indirerek daha güçlü bir sınıflandırma performansı sağlaması olduğu düşünülmektedir. Bu çalışmada bu iki veri madenciliği yönteminin Eğitim Bilimleri alanında kullanılmasının yararları ortaya konulmuş ve bu alanda ilgili yöntemlerin katkısı tartışılmıştır

Fractal Dimension as a Predictor of Organizational Change Success

As many as two thirds of organizational change (OC) initiatives fail to achieve their outcome objectives. Researchers have demonstrated that successful change requires alignment among all levels of an organization. However, contemporary OC models do not quantify the degree of hierarchical alignment during the change process. The purpose of this quantitative, correlational study was to examine whether the fractal dimension of hierarchical alignment (predictor variable) was associated with OC success (criterion variable) as described by the self-organizing fractal theory (SOFT). The research question addressed the association between the fractal dimension related to the alignment of OC beliefs and behavioral intentions across an organizational hierarchy and subsequent OC success. The instrument included creolization and change resistance themes to collect primary survey data through the self-selection of 125 North American aerospace workers who had participated in a formal change process. Pearson’s product-moment, Spearman rank, and Kendall’s tau correlation coefficients revealed a strong positive association between fractal dimension and OC success. Subsequent regression analysis reinforced the positive correlation and explained at least 56% of the observed variation in OC success. The results contributed to scholarly OC research by providing proof-of-concept demonstration that SOFT is applicable to OC research. This study also contributed to social change by creating measures that may lead to improved change management, resulting in less resource waste, lower employee stress, and improved change outcomes

Assessment of Students’ Achievements and Competencies in Mathematics Using CART and CART Ensembles and Bagging with Combined Model Improvement by MARS

The aim of this study is to evaluate students’ achievements in mathematics using three machine learning regression methods: classification and regression trees (CART), CART ensembles and bagging (CART-EB) and multivariate adaptive regression splines (MARS). A novel ensemble methodology is proposed based on the combination of CART and CART-EB models in a new ensemble to regress the actual data using MARS. Results of a final exam test, control and home assignments, and other learning activities to assess students’ knowledge and competencies in applied mathematics are examined. The exam test combines problems on elements of mathematical analysis, statistics and a small practical project. The project is the new competence-oriented element, which requires students to formulate problems themselves, to choose different solutions and to use or not use specialized software. Initially, empirical data are statistically modeled using six CART and six CART-EB competing models. The models achieve a goodness-of-fit up to 96% to actual data. The impact of the examined factors on the students’ success at the final exam is determined. Using the best of these models and proposed novel ensemble procedure, final MARS models are built that outperform the other models for predicting the achievements of students in applied mathematics