A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method

In this article, an algorithm is proposed for creating an ensemble classifier. The name of the algorithm is the F-score subspace method (FsBoost). According to this method, the features are selected with the F-score and classified with different or the same classifiers. In the next step, the ensemble classifier is created. Two versions that are named FsBoost.V1 and FsBoost.V2 have been developed based on classification by the same or different classifiers. According to the results obtained, the results are consistent with the literature. Besides, a higher accuracy rate is obtained compared with many algorithms in the literature. The algorithm is fast because it has a few steps. It is thought that the algorithm will be successful due to these advantages.

[1]  Kemal Polat,et al.  The Effect of Training and Testing Process on Machine Learning in Biomedical Datasets , 2020, Mathematical Problems in Engineering.

[2]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[3]  Yanhui Guo,et al.  Classification of multi-carrier digital modulation signals using NCM clustering based feature-weighting method , 2019, Comput. Ind..

[4]  Kemal Polat,et al.  A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis , 2007, Comput. Biol. Medicine.

[5]  Mohamed El Bachir Menai,et al.  An Individualized Preprocessing for Medical Data Classification , 2016 .

[6]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[7]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Nihat Daldal,et al.  A novel demodulation method for quadrate type modulations using a hybrid signal processing method , 2020 .

[9]  Shulin Wang,et al.  Feature selection in machine learning: A new perspective , 2018, Neurocomputing.

[10]  Muhammed Kürşad Uçar,et al.  Classification Performance-Based Feature Selection Algorithm for Machine Learning: P-Score , 2020 .

[11]  Adeeb Noor.  Discovering Gaps in Saudi Education for Digital Health Transformation , 2019 .

[12]  Kemal Polat,et al.  Deep Learning Applications for Hyperspectral Imaging: A Systematic Review , 2020, Journal of the Institute of Electronics and Computer.

[13]  A. Barnawi,et al.  D4: Deep Drug-drug interaction Discovery and Demystification , 2020, bioRxiv.

[14]  Azah Mohamed,et al.  Islanding detection in a distributed generation integrated power system using phase space technique and probabilistic neural network , 2015, Neurocomputing.

[15]  Kemal Polat,et al.  Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques , 2016, Neural Computing and Applications.

[16]  Kemal Polat,et al.  Automatic determination of digital modulation types with different noises using Convolutional Neural Network based on time-frequency information , 2020, Appl. Soft Comput..

[17]  Donghai Guan,et al.  A Review of Ensemble Learning Based Feature Selection , 2014 .

[18]  Lior Rokach,et al.  Ensemble-based classifiers , 2010, Artificial Intelligence Review.

[19]  Kemal Polat,et al.  A new feature selection method on classification of medical datasets: Kernel F-score feature selection , 2009, Expert Syst. Appl..

[20]  Burhan Ergen,et al.  A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models , 2020, IRBM.

[21]  Kemal Polat,et al.  Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism , 2007, Expert Syst. Appl..

[22]  Kemal Polat,et al.  Binary particle swarm optimization (BPSO) based channel selection in the EEG signals and its application to speller systems , 2020, Journal of Artificial Intelligence and Systems.

[23]  Adeeb Noor,et al.  Drug-drug interaction discovery and demystification using Semantic Web technologies , 2017, J. Am. Medical Informatics Assoc..

[24]  Lior Rokach,et al.  Ensemble methods for multi-label classification , 2013, Expert Syst. Appl..

[25]  Kemal Polat,et al.  Detection of Skin Diseases from Dermoscopy Image Using the combination of Convolutional Neural Network and One-versus-All , 2020, Journal of Artificial Intelligence and Systems.

[26]  Kemal Polat,et al.  A novel demodulation structure for quadrate modulation signals using the segmentary neural network modelling , 2020 .