Incremental Learning of SVM Using Backward Elimination and Forward Selection of Support Vectors

2019 ◽  
Author(s):  
Venkata Pesala ◽  
Arun Kumar Kalakanti ◽  
Topon Paul ◽  
Ken Ueno ◽  
Ankit Kesarwani ◽  
...  
Keyword(s):  
Incremental Learning ◽  
Forward Selection ◽  
Backward Elimination ◽  
Support Vectors ◽  
Selection Of

scholarly journals Transfer Incremental Learning Using Data Augmentation

2018 ◽  
Vol 8 (12) ◽  
pp. 2512 ◽  
Author(s):  
Ghouthi Boukli Hacene ◽  
Vincent Gripon ◽  
Nicolas Farrugia ◽  
Matthieu Arzel ◽  
Michel Jezequel
Keyword(s):  
Incremental Learning ◽  
Deep Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Low Complexity ◽  
Computational Power ◽  
Learning Problem ◽  
Learning Techniques ◽  
Using Data ◽  
Selection Of

Deep learning-based methods have reached state of the art performances, relying on a large quantity of available data and computational power. Such methods still remain highly inappropriate when facing a major open machine learning problem, which consists of learning incrementally new classes and examples over time. Combining the outstanding performances of Deep Neural Networks (DNNs) with the flexibility of incremental learning techniques is a promising venue of research. In this contribution, we introduce Transfer Incremental Learning using Data Augmentation (TILDA). TILDA is based on pre-trained DNNs as feature extractors, robust selection of feature vectors in subspaces using a nearest-class-mean based technique, majority votes and data augmentation at both the training and the prediction stages. Experiments on challenging vision datasets demonstrate the ability of the proposed method for low complexity incremental learning, while achieving significantly better accuracy than existing incremental counterparts.

scholarly journals Data Mining Performance of Toddler Nutrition Classification Based on Family Nutrition Awareness and Human Development Index

2020 ◽  
Vol 8 (5) ◽  
pp. 1591-1596
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Health Workers ◽  
Data Mining Algorithm ◽  
Forward Selection ◽  
Central Java ◽  
Backward Elimination ◽  
Best Parameter ◽  
Using Data ◽  
Family Nutrition

Nutrition problems that occurred in districts/cities of Central Java province from 2015-2017 were only 1 district city that did not have nutritional problems (good category) in 2015.The rest had acute, chronic or acute chronic nutrition problems. The search for the most influential attributes in toddler nutrition problems using data mining is expected to help health workers to focus more on solving problems based on classification in the area.Therefore, improving the nutritional status of the community can be accelerated. The best parameter search from the selection of features and data mining algorithm using the Optimize Parameters (Grid) operator found in Rapidminer.The feature selection models used are Backward Elimination, Forward Selection, and Optimize Selection. The datamining algorithm used is Naive Bayes, Decision Tree, k-NN, and Neural Network.The merging of the feature selection model and the datamining algorithm resulted in 12 algorithm models used in this study.The best model that was processed using test data with the highest accuracy of 74.19% was obtained from backward-neural network elimination. The attribute that is not very influential based on the model obtained is the condition of the mother who died.

scholarly journals Machine learning and automatic selection of attributes for the identification of Chagas disease from clinical and sociodemographic data

2021 ◽  
Vol 10 (4) ◽  
pp. e19310413879
Author(s):  
Weber de Santana Teles ◽  
Aydano Pamponet Machado ◽  
Paulo Celso Curvelo Cantos Júnior ◽  
Cláudia Moura de Melo ◽  
Maria Hozana Santos Silva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Chagas Disease ◽  
Disease Diagnosis ◽  
Forward Selection ◽  
Treatment And Prevention ◽  
Sociodemographic Data ◽  
Automatic Selection ◽  
Sensitivity Specificity ◽  
Selection Of

Objective: evaluate the potential use of machine learning and the automatic selection of attributes in discrimination of individuals with and without Chagas disease based on clinical and sociodemographic data. Method: After the evaluation of many learning algorithms, they have been chosen and the comparison between neural network Multilayer Perceptron (MLP) and the Linear Regression (LR) was done, seeking which one presents the best performance for prediction of the Chagas disease diagnosis, being used the criteria of sensitivity, specificity, accuracy and area under the ROC curve (AUC). Generated models were also compared, using the methods of automatic selection of attributes: Forward Selection, Backward Elimination and genetic algorithm. Results: The best results were achieved using the genetic algorithm and the MLP presented accuracy of 95.95%, 78.30% sensitivity, and specificity of 75.00% and AUC of 0.861. Conclusion: It was proved to be a very interesting performance, given the nature of the data used for sorting and use in public health, glimpsing its relevance in the medical field, enabling an approximation of prevalence that justifies the actions of active search of individuals Chagas disease patients for treatment and prevention.

scholarly journals Optimal Boundary SVM Incremental Learning Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 2957-2962 ◽  
Author(s):  
Jian Cao ◽  
Shi Yu Sun ◽  
Xiu Sheng Duan
Keyword(s):  
Support Vector Machine ◽  
Learning Process ◽  
Incremental Learning ◽  
Learning Algorithm ◽  
Identical Result ◽  
Support Vector ◽  
Optimal Boundary ◽  
Support Vectors ◽  
Sample Classification ◽  
Depth Analysis

Support vectors (SVs) cant be selected completely in support vector machine (SVM) incremental, resulting incremental learning process cant be sustained. In order to solve this problem, the article proposes optimal boundary SVM incremental learning algorithm. Based on in-depth analysis of the trend of the classification surface and make use of the KKT conditions, selecting the border of the vectors include the support vectors to participate SVM incremental learning. The experiment shows that the algorithm can be completely covered the support vectors and have the identical result with the classic support vector machine, it also saves lots of time. Therefore it can provide the conditions for future large sample classification and incremental learning sustainability.

scholarly journals Feature Selection Based on Machine Learning in MRIs for Hippocampal Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Sabina Tangaro ◽  
Nicola Amoroso ◽  
Massimo Brescia ◽  
Stefano Cavuoti ◽  
Andrea Chincarini ◽  
...  
Keyword(s):  
Feature Selection ◽  
Neurodegenerative Diseases ◽  
Structural Changes ◽  
Brain Magnetic Resonance Imaging ◽  
Independent Set ◽  
Filter Method ◽  
Forward Selection ◽  
Backward Elimination ◽  
Mri Scans ◽  
Embedded Method

Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic resonance imaging (MRI) scans can show these variations and therefore can be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust, and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach; for each voxel a number of local features were calculated. In this paper, we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) sequential forward selection and (iii) sequential backward elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 feature for each voxel (sequential backward elimination) we obtained comparable state-of-the-art performances with respect to the standard tool FreeSurfer.

scholarly journals Feature Selection via Coalitional Game Theory

2007 ◽  
Vol 19 (7) ◽  
pp. 1939-1961 ◽  
Author(s):  
Shay Cohen ◽  
Gideon Dror ◽  
Eytan Ruppin
Keyword(s):  
Game Theory ◽  
Feature Selection ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Data Sets ◽  
Selection Algorithm ◽  
Forward Selection ◽  
Empirical Comparison ◽  
Backward Elimination ◽  
Unseen Data

We present and study the contribution-selection algorithm (CSA), a novel algorithm for feature selection. The algorithm is based on the multiperturbation shapley analysis (MSA), a framework that relies on game theory to estimate usefulness. The algorithm iteratively estimates the usefulness of features and selects them accordingly, using either forward selection or backward elimination. It can optimize various performance measures over unseen data such as accuracy, balanced error rate, and area under receiver-operator-characteristic curve. Empirical comparison with several other existing feature selection methods shows that the backward elimination variant of CSA leads to the most accurate classification results on an array of data sets.

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