Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography
L Rokach - Computational statistics & data analysis, 2009 - Elsevier
Ensemble methodology, which builds a classification model by integrating multiple
classifiers, can be used for improving prediction performance. Researchers from various …
classifiers, can be used for improving prediction performance. Researchers from various …
Ensemble feature selection for high-dimensional data: a stability analysis across multiple domains
B Pes - Neural Computing and Applications, 2020 - Springer
Selecting a subset of relevant features is crucial to the analysis of high-dimensional datasets
coming from a number of application domains, such as biomedical data, document and …
coming from a number of application domains, such as biomedical data, document and …
[图书][B] Data mining with decision trees: theory and applications
Decision trees have become one of the most powerful and popular approaches in
knowledge discovery and data mining; it is the science of exploring large and complex …
knowledge discovery and data mining; it is the science of exploring large and complex …
[图书][B] Pattern classification using ensemble methods
L Rokach - 2009 - books.google.com
Researchers from various disciplines such as pattern recognition, statistics, and machine
learning have explored the use of ensemble methodology since the late seventies. Thus …
learning have explored the use of ensemble methodology since the late seventies. Thus …
[图书][B] Ensemble learning: pattern classification using ensemble methods
L Rokach - 2019 - World Scientific
Artificial intelligence (AI) is a scientific discipline that aims to create intelligent machines.
Machine learning is a popular and practical AI subfield that aims to automatically improve …
Machine learning is a popular and practical AI subfield that aims to automatically improve …
Exploiting the ensemble paradigm for stable feature selection: a case study on high-dimensional genomic data
B Pes, N Dessì, M Angioni - Information fusion, 2017 - Elsevier
Ensemble classification is a well-established approach that involves fusing the decisions of
multiple predictive models. A similar “ensemble logic” has been recently applied to …
multiple predictive models. A similar “ensemble logic” has been recently applied to …
Detection of unknown computer worms based on behavioral classification of the host
Machine learning techniques are widely used in many fields. One of the applications of
machine learning in the field of information security is classification of a computer behavior …
machine learning in the field of information security is classification of a computer behavior …
Survey on stability of feature selection
刘艺， 曹建军， 刁兴春， 周星 - Journal of software, 2017 - jos.org.cn
随着大数据的发展和机器学习的广泛应用, 各行业的数据量呈现大规模的增长,
高维性是这些数据的重要特点, 采用特征选择对高维数据进行降维是一种预处理方法 …
高维性是这些数据的重要特点, 采用特征选择对高维数据进行降维是一种预处理方法 …
Detecting unknown computer worm activity via support vector machines and active learning
To detect the presence of unknown worms, we propose a technique based on computer
measurements extracted from the operating system. We designed a series of experiments to …
measurements extracted from the operating system. We designed a series of experiments to …
Leveraging virtual machine introspection with memory forensics to detect and characterize unknown malware using machine learning techniques at hypervisor
MAA Kumara, CD Jaidhar - Digital Investigation, 2017 - Elsevier
Abstract The Virtual Machine Introspection (VMI) has emerged as a fine-grained, out-of-VM
security solution that detects malware by introspecting and reconstructing the volatile …
security solution that detects malware by introspecting and reconstructing the volatile …