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A Lightweight Machine Learning Framework for Post-Stroke Gait Abnormality Classification Using Wearable Gyroscope Features. [PDF]
Sensors (Basel)Orfanos S +5 more
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Improved Random Forest for Classification
IEEE Transactions on Image Processing, 2018We propose an improved random forest classifier that performs classification with minimum number of trees. The proposed method iteratively removes some unimportant features. Based on the number of important and unimportant features, we formulate a novel theoretical upper limit on the number of trees to be added to the forest to ensure improvement in ...
Dipti Prasad Mukherjee +2 more
exaly +3 more sources
Cancer classification using Rotation Forest
Computers in Biology and Medicine, 2008We address the microarray dataset based cancer classification using a newly proposed multiple classifier system (MCS), referred to as Rotation Forest. To the best of our knowledge, it is the first time that Rotation Forest has been applied to the microarray dataset classification.
De-Shuang Huang, Kun-Hong Liu
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Improving Classification Trustworthiness in Random Forests
2021 IEEE International Conference on Cyber Security and Resilience (CSR), 2021Machine learning algorithms are becoming more and more widespread in industrial as well as in societal settings. This diffusion is starting to become a critical aspect of new software-intensive applications due to the need of fast reactions to changes, even if temporary, in data.
De Biase M. S. +3 more
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Kernel Rotation Forests for Classification
2020 IEEE International Conference on Big Data and Smart Computing (BigComp), 2020There have been significant research efforts for developing decision tree (DT)-based ensemble methods. Such methods generally construct an ensemble by aggregating a large number of unpruned DTs, thereby yielding good classification accuracy. A recently developed method, rotation forest, is known to achieve better classification accuracy by rotating the
Jaewoong Shim +2 more
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An imprecise deep forest for classification
Expert Systems with Applications, 2020Abstract An imprecise deep forest classifier, which can be regarded as a modification of the deep forest proposed by Zhou and Feng, is presented in the paper. In the proposed classifier, the precise class probabilities at leaf nodes of decision trees in the deep forest are replaced with interval-valued probabilities produced by Walley’s imprecise ...
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Proactive Forest for Supervised Classification
2018Random Forest is one of the most used and accurate ensemble methods based on decision trees. Since diversity is a necessary condition to build a good ensemble, Random Forest selects a random feature subset for building decision nodes. This generation procedure could cause important features to be selected in multiple trees in the ensemble, decreasing ...
Nayma Cepero-Pérez +4 more
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Tailoring Random Forest for Requirements Classification
2020Automated and semi-automated classifications of requirements (type and topics) are important for making requirements management more efficient. We report how we tailored a random forest approach in the EU funded project OpenReq, aiming for sufficient quality for practical use in bid projects. Evaluation with thirty thousand requirements in English from
Andreas A. Falkner +2 more
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