Andrews curves Applied Machine Learning Bagging Boosting Business intelligence Chomsky normal form Class-imbalanced problems Classifier ensembles Cocke-Younger-Kasami algorithm Computer Science teaching Data analysis Data Mining Data visualization Decision trees Disturbing neighbors End of studies project Ensemble methods Ensembles Exploratory data analysis Exploratory projection pursuit Fault diagnosis Finite automata Grammars Imbalanced data Instance selection Linear projections LL parsing Machine learning Neural networks Parsing algorithms Random forest Random oracles Regression Regression ensembles Regression trees Regular expressions Rotation forest Self organizing maps Support vector machines surface roughness
2018 |
Gunn, Iain A D; Arnaiz-González, Álvar; Kuncheva, Ludmila I A taxonomic look at instance-based stream classifiers Journal Article In: Neurocomputing, 286 , pp. 167-178, 2018, ISSN: 0925-2312. Abstract | Links | BibTeX | Tags: Concept drift, Instance selection, Machine learning, Prototype generation, Stream classification @article{Gunn2018, title = {A taxonomic look at instance-based stream classifiers}, author = {Iain A D Gunn and Álvar Arnaiz-González and Ludmila I Kuncheva}, url = {https://www.sciencedirect.com/science/article/pii/S092523121830095X}, doi = {10.1016/j.neucom.2018.01.062}, issn = {0925-2312}, year = {2018}, date = {2018-04-19}, journal = {Neurocomputing}, volume = {286}, pages = {167-178}, abstract = {Large numbers of data streams are today generated in many fields. A key challenge when learning from such streams is the problem of concept drift. Many methods, including many prototype methods, have been proposed in recent years to address this problem. This paper presents a refined taxonomy of instance selection and generation methods for the classification of data streams subject to concept drift. The taxonomy allows discrimination among a large number of methods which pre-existing taxonomies for offline instance selection methods did not distinguish. This makes possible a valuable new perspective on experimental results, and provides a framework for discussion of the concepts behind different algorithm-design approaches. We review a selection of modern algorithms for the purpose of illustrating the distinctions made by the taxonomy. We present the results of a numerical experiment which examined the performance of a number of representative methods on both synthetic and real-world data sets with and without concept drift, and discuss the implications for the directions of future research in light of the taxonomy. On the basis of the experimental results, we are able to give recommendations for the experimental evaluation of algorithms which may be proposed in the future.}, keywords = {Concept drift, Instance selection, Machine learning, Prototype generation, Stream classification}, pubstate = {published}, tppubtype = {article} } Large numbers of data streams are today generated in many fields. A key challenge when learning from such streams is the problem of concept drift. Many methods, including many prototype methods, have been proposed in recent years to address this problem. This paper presents a refined taxonomy of instance selection and generation methods for the classification of data streams subject to concept drift. The taxonomy allows discrimination among a large number of methods which pre-existing taxonomies for offline instance selection methods did not distinguish. This makes possible a valuable new perspective on experimental results, and provides a framework for discussion of the concepts behind different algorithm-design approaches. We review a selection of modern algorithms for the purpose of illustrating the distinctions made by the taxonomy. We present the results of a numerical experiment which examined the performance of a number of representative methods on both synthetic and real-world data sets with and without concept drift, and discuss the implications for the directions of future research in light of the taxonomy. On the basis of the experimental results, we are able to give recommendations for the experimental evaluation of algorithms which may be proposed in the future. |
Güemes-Peña, Diego; López-Nozal, Carlos; Marticorena-Sánchez, Raúl; Maudes-Raedo, Jesús Emerging topics in mining software repositories Journal Article In: Progress in Artificial Intelligence, pp. 1-11, 2018, ISSN: 2192-6360. Abstract | Links | BibTeX | Tags: Data Mining, Machine learning, Software engineering, Software process, Software repository @article{Güemes-Peña2018, title = {Emerging topics in mining software repositories}, author = {Diego Güemes-Peña and Carlos López-Nozal and Raúl Marticorena-Sánchez and Jesús Maudes-Raedo}, url = {https://link.springer.com/content/pdf/10.1007/s13748-018-0147-7.pdf}, doi = {10.1007/s13748-018-0147-7}, issn = {2192-6360}, year = {2018}, date = {2018-01-01}, journal = {Progress in Artificial Intelligence}, pages = {1-11}, abstract = {A software process is a set of related activities that culminates in the production of a software package: specification, design, implementation, testing, evolution into new versions, and maintenance. There are also other supporting activities such as configuration and change management, quality assurance, project management, evaluation of user experience, etc. Software repositories are infrastructures to support all these activities. They can be composed with several systems that include code change management, bug tracking, code review, build system, release binaries, wikis, forums, etc. This position paper on mining software repositories presents a review and a discussion of research in this field over the past decade. We also identify applied machine learning strategies, current working topics, and future challenges for the improvement of company decision-making systems. Machine learning is defined as the process of discovering patterns in data. It can be applied to software repositories, since every change is recorded as data. Companies can then use these patterns as the basis for their decision-making systems and for knowledge discovery.}, keywords = {Data Mining, Machine learning, Software engineering, Software process, Software repository}, pubstate = {published}, tppubtype = {article} } A software process is a set of related activities that culminates in the production of a software package: specification, design, implementation, testing, evolution into new versions, and maintenance. There are also other supporting activities such as configuration and change management, quality assurance, project management, evaluation of user experience, etc. Software repositories are infrastructures to support all these activities. They can be composed with several systems that include code change management, bug tracking, code review, build system, release binaries, wikis, forums, etc. This position paper on mining software repositories presents a review and a discussion of research in this field over the past decade. We also identify applied machine learning strategies, current working topics, and future challenges for the improvement of company decision-making systems. Machine learning is defined as the process of discovering patterns in data. It can be applied to software repositories, since every change is recorded as data. Companies can then use these patterns as the basis for their decision-making systems and for knowledge discovery. |