A large empirical assessment of the role of data balancing in machine-learning-based code smell detection

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

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Leveraging Machine Learning to Characterize the Role of Socio-Economic Determinants on Physical Health and Well-Being Among Veterans

SSRN Electronic Journal ◽

10.2139/ssrn.3686845 ◽

2020 ◽

Author(s):

Christos Makridis ◽

David Zhao ◽

Cosmin (Adi) Bejan ◽

Gil Alterovitz

Keyword(s):

Machine Learning ◽

Physical Health ◽

Well Being ◽

Economic Determinants ◽

Health And Well Being

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A Review on Machine-Learning Based Code Smell Detection Techniquesin Object-Oriented Software System(s)

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096513999200922125839 ◽

2020 ◽

Vol 13 ◽

Author(s):

Amandeep Kaur ◽

Sushma Jain ◽

Shivani Goel ◽

Gaurav Dhiman

Keyword(s):

Machine Learning ◽

Programming Languages ◽

Software Quality ◽

Empirical Studies ◽

Statistical Testing ◽

Machine Learning Techniques ◽

Support Vector ◽

Code Smells ◽

Detection Techniques ◽

Code Smell

Context: Code smells are symptoms, that something may be wrong in software systems that can cause complications in maintaining software quality. In literature, there exists many code smells and their identification is far from trivial. Thus, several techniques have also been proposed to automate code smell detection in order to improve software quality. Objective: This paper presents an up-to-date review of simple and hybrid machine learning based code smell detection techniques and tools. Methods: We collected all the relevant research published in this field till 2020. We extracted the data from those articles and classified them into two major categories. In addition, we compared the selected studies based on several aspects like, code smells, machine learning techniques, datasets, programming languages used by datasets, dataset size, evaluation approach, and statistical testing. Results: Majority of empirical studies have proposed machine- learning based code smell detection tools. Support vector machine and decision tree algorithms are frequently used by the researchers. Along with this, a major proportion of research is conducted on Open Source Softwares (OSS) such as, Xerces, Gantt Project and ArgoUml. Furthermore, researchers paid more attention towards Feature Envy and Long Method code smells. Conclusion: We identified several areas of open research like, need of code smell detection techniques using hybrid approaches, need of validation employing industrial datasets, etc.

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