Improving performance with hybrid feature selection and ensemble machine learning techniques for code smell detection

2021 ◽  
pp. 102713
Author(s):  
Shivani Jain ◽  
Anju Saha
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Machine Learning Techniques ◽  
Ensemble Machine Learning ◽  
Learning Techniques ◽  
Code Smell

scholarly journals Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques

Inventions ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 57
Author(s):  
Attique Ur Rehman ◽  
Tek Tjing Lie ◽  
Brice Vallès ◽  
Shafiqur Rahman Tito
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Water Heating ◽  
Energy Monitoring ◽  
Non Invasive ◽  
Ensemble Machine Learning ◽  
Learning Techniques ◽  
Load Monitoring

The recent advancement in computational capabilities and deployment of smart meters have caused non-intrusive load monitoring to revive itself as one of the promising techniques of energy monitoring. Toward effective energy monitoring, this paper presents a non-invasive load inference approach assisted by feature selection and ensemble machine learning techniques. For evaluation and validation purposes of the proposed approach, one of the major residential load elements having solid potential toward energy efficiency applications, i.e., water heating, is considered. Moreover, to realize the real-life deployment, digital simulations are carried out on low-sampling real-world load measurements: New Zealand GREEN Grid Database. For said purposes, MATLAB and Python (Scikit-Learn) are used as simulation tools. The employed learning models, i.e., standalone and ensemble, are trained on a single household’s load data and later tested rigorously on a set of diverse households’ load data, to validate the generalization capability of the employed models. This paper presents a comprehensive performance evaluation of the presented approach in the context of event detection, feature selection, and learning models. Based on the presented study and corresponding analysis of the results, it is concluded that the proposed approach generalizes well to the unseen testing data and yields promising results in terms of non-invasive load inference.

scholarly journals Learning from Imbalanced Educational Data Using Ensemble Machine Learning Algorithms

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 01) ◽  
pp. 183-195
Author(s):  
Thingbaijam Lenin ◽  
N. Chandrasekaran
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Missing Values ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Adaptive Boosting ◽  
Stochastic Gradient Boosting ◽  
Ensemble Machine Learning ◽  
Learning Techniques ◽  
Student’S Performance

Student’s academic performance is one of the most important parameters for evaluating the standard of any institute. It has become a paramount importance for any institute to identify the student at risk of underperforming or failing or even drop out from the course. Machine Learning techniques may be used to develop a model for predicting student’s performance as early as at the time of admission. The task however is challenging as the educational data required to explore for modelling are usually imbalanced. We explore ensemble machine learning techniques namely bagging algorithm like random forest (rf) and boosting algorithms like adaptive boosting (adaboost), stochastic gradient boosting (gbm), extreme gradient boosting (xgbTree) in an attempt to develop a model for predicting the student’s performance of a private university at Meghalaya using three categories of data namely demographic, prior academic record, personality. The collected data are found to be highly imbalanced and also consists of missing values. We employ k-nearest neighbor (knn) data imputation technique to tackle the missing values. The models are developed on the imputed data with 10 fold cross validation technique and are evaluated using precision, specificity, recall, kappa metrics. As the data are imbalanced, we avoid using accuracy as the metrics of evaluating the model and instead use balanced accuracy and F-score. We compare the ensemble technique with single classifier C4.5. The best result is provided by random forest and adaboost with F-score of 66.67%, balanced accuracy of 75%, and accuracy of 96.94%.

scholarly journals Evolutionary Machine Learning for Classification with Incomplete Data

2021 ◽  
Author(s):  
◽  
Cao Truong Tran
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Genetic Programming ◽  
Incomplete Data ◽  
Missing Values ◽  
Machine Learning Techniques ◽  
Feature Construction ◽  
Classification Algorithms ◽  
Learning Techniques ◽  
Effectiveness And Efficiency

<p>Classification is a major task in machine learning and data mining. Many real-world datasets suffer from the unavoidable issue of missing values. Classification with incomplete data has to be carefully handled because inadequate treatment of missing values will cause large classification errors.    Existing most researchers working on classification with incomplete data focused on improving the effectiveness, but did not adequately address the issue of the efficiency of applying the classifiers to classify unseen instances, which is much more important than the act of creating classifiers. A common approach to classification with incomplete data is to use imputation methods to replace missing values with plausible values before building classifiers and classifying unseen instances. This approach provides complete data which can be then used by any classification algorithm, but sophisticated imputation methods are usually computationally intensive, especially for the application process of classification. Another approach to classification with incomplete data is to build a classifier that can directly work with missing values. This approach does not require time for estimating missing values, but it often generates inaccurate and complex classifiers when faced with numerous missing values. A recent approach to classification with incomplete data which also avoids estimating missing values is to build a set of classifiers which then is used to select applicable classifiers for classifying unseen instances. However, this approach is also often inaccurate and takes a long time to find applicable classifiers when faced with numerous missing values.   The overall goal of the thesis is to simultaneously improve the effectiveness and efficiency of classification with incomplete data by using evolutionary machine learning techniques for feature selection, clustering, ensemble learning, feature construction and constructing classifiers.   The thesis develops approaches for improving imputation for classification with incomplete data by integrating clustering and feature selection with imputation. The approaches improve both the effectiveness and the efficiency of using imputation for classification with incomplete data.   The thesis develops wrapper-based feature selection methods to improve input space for classification algorithms that are able to work directly with incomplete data. The methods not only improve the classification accuracy, but also reduce the complexity of classifiers able to work directly with incomplete data.   The thesis develops a feature construction method to improve input space for classification algorithms with incomplete data by proposing interval genetic programming-genetic programming with a set of interval functions. The method improves the classification accuracy and reduces the complexity of classifiers.   The thesis develops an ensemble approach to classification with incomplete data by integrating imputation, feature selection, and ensemble learning. The results show that the approach is more accurate, and faster than previous common methods for classification with incomplete data.   The thesis develops interval genetic programming to directly evolve classifiers for incomplete data. The results show that classifiers generated by interval genetic programming can be more effective and efficient than classifiers generated the combination of imputation and traditional genetic programming. Interval genetic programming is also more effective than common classification algorithms able to work directly with incomplete data.    In summary, the thesis develops a range of approaches for simultaneously improving the effectiveness and efficiency of classification with incomplete data by using a range of evolutionary machine learning techniques.</p>

Machine learning techniques for code smell detection: A systematic literature review and meta-analysis

2019 ◽  
Vol 108 ◽  
pp. 115-138 ◽  
Author(s):  
Muhammad Ilyas Azeem ◽  
Fabio Palomba ◽  
Lin Shi ◽  
Qing Wang
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Code Smell

scholarly journals Feature Selection and Classification of Leukemia Cancer Using Machine Learning Techniques

2020 ◽  
Vol 5 (2) ◽  
pp. 18
Author(s):  
Md. Alamgir Sarder ◽  
Md. Maniruzzaman ◽  
Benojir Ahammed
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals A Survey on Diagnosis and Analysis of Diabetic Retinopathy using Feature Selection

2020 ◽  
pp. 170-176
Author(s):  
Amalu Michael ◽  
Deepa S S
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Diabetic Retinopathy ◽  
High Ratio ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Diabetic Eye Disease ◽  
Selection Mechanisms ◽  
Feature Selection Techniques

Diabetic retinopathy is one of the common forms of diabetic eye disease. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal vessels. Machine learning may be a broad multidisciplinary field that has its roots in statistics, algebra, data processing, and information analytics, etc. Machine learning is used to discover patterns from medical data and provide an efficient way to predict diseases.ML is an application of artificial intelligence it collects information from training data. There are several machine learning techniques are used for the diagnosis of diabetic retinopathy. This paper mainly focuses on the survey of such techniques and also various feature selection mechanisms. This study provides the basic categorization of feature selection techniques and discussing their use.

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