Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms

SVM-PSO based Feature Selection for Improving Medical Diagnosis Reliability using Machine Learning Ensembles

“…However in RF each tree is trained with complete data set with a rotated feature space. As the algorithm builds classifiers use hyperplanes parallel to the feature axes and a small rotation of the axes lead to diverse trees [1,8,17]. More explicitly, the structure of the RF algorithm is given as follows: Rotation forest algorithm Let X be the training sample set.…”

“…Various ensemble learning strategies found in machine learning literature are composite classifier systems, mixture of experts, consensus aggregation, dynamic classifier selection, classifier fusion and committees of neural networks. Several Computer Aided Diagnosis system applications use classifier ensembles (especially Rotation Forest algorithm) to increase accuracy of convenient classifiers [8]. Besides the choice of base learner classifiers, the predictive performance of multiple classifier system is largely influenced by the degree of diversity of community of base learners constituting the ensemble.…”

“…The wrapper technique finds feature subsets based on the performance of a preselected classification algorithm on a training data set, whereas the filter method depends on the properties of the features to be selected to form the best feature subset. The selection of a subset of relevant features, both strategies uses a search method such as individual ranking, genetic search, forward search and backward search etc [6][7][8].…”

Applying learning algorithms to extract anxiety levels using the heart rate variability measure

“…Five attributes were then processed by Weka (a machine learning based software). [18][19][20][21][22][23][24] In the present study, to achieve the optimization of the results previously found we focused in an ECG analysis using wavelet transforms, statistical information and the original signal processed by LWL, J48, ADTree and RandomForest. These algorithms are inserted in Weka and have already been used in signal processing at different context/applications, as an open source software Weka has a high potential in many other applicantions.…”

An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳s disease

“…In [20], Ozcift et al combined the correlation based feature selection (CFS) algorithm with the RF ensemble classifiers of 30 machine learning algorithms to identify PD, and the best classification accuracy of 87.13% was achieved by the proposed CFS-RF model. In [21], Spadoto et al applied evolutionary-based techniques in combination with the Optimum-Path Forest (OPF) classifier to detect PD, and the best classification accuracy of 84.01% was achieved.…”