Comparative study of Principal Component Analysis based Intrusion Detection approach using machine learning algorithms

2015 ◽  
Author(s):  
Krupa Joel Chabathula ◽  
C. D. Jaidhar ◽  
M. A. Ajay Kumara
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Intrusion Detection ◽  
Comparative Study ◽  
Learning Algorithms ◽  
Principal Component ◽  
Component Analysis ◽  
Machine Learning Algorithms ◽  
Detection Approach

scholarly journals Data Mining and Principal Component Analysis on Coimbra Breast Cancer Dataset

2021 ◽  
Author(s):  
Anupam Sen
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Breast Cancer Dataset ◽  
Analysis Tool ◽  
Machine Learning Classification

Machine Learning (ML) techniques play an important role in the medical field. Early diagnosis is required to improve the treatment of carcinoma. During this analysis Breast Cancer Coimbra dataset (BCCD) with ten predictors are analyzed to classify carcinoma. In this paper method for feature selection and Machine learning algorithms are applied to the dataset from the UCI repository. WEKA (“Waikato Environment for Knowledge Analysis”) tool is used for machine learning techniques. In this paper Principal Component Analysis (PCA) is used for feature extraction. Different Machine Learning classification algorithms are applied through WEKA such as Glmnet, Gbm, ada Boosting, Adabag Boosting, C50, Cforest, DcSVM, fnn, Ksvm, Node Harvest compares the accuracy and also compare values such as Kappa statistic, Mean Absolute Error (MAE), Root Mean Square Error (RMSE). Here the 10-fold cross validation method is used for training, testing and validation purposes.

scholarly journals Radiomics Analysis Using Stability Selection Supervised Principal Component Analysis for Right-censored Survival Data

2018 ◽  
Author(s):  
Kang K. Yan ◽  
Xiaofei Wang ◽  
Wendy Lam ◽  
Varut Vardhanabhuti ◽  
Anne W.M. Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Prognostic Markers ◽  
Principal Component ◽  
Component Analysis ◽  
Machine Learning Algorithms ◽  
Survival Outcomes ◽  
Imaging Features ◽  
Biomedical Images ◽  
Stability Selection

AbstractRadiomics is a newly emerging field that involves the extraction of a large number of quantitative features from biomedical images through the use of data-characterization algorithms. Radiomics provides a noninvasive approach for personalized therapy decision by identifying distinctive imaging features for predicting prognosis and therapeutic response. So far, many of the published radiomics studies utilize existing out of the box algorithms to identify the prognostic markers from biomedical images that are not specific to radiomics data. T o better utilize biomedical image, we propose a novel machine learning approach, stability selection supervised principal component analysis (SSSuperPCA) that identify a set of stable features from radiomics big data coupled with dimension reduction for right censored survival outcomes. In this paper, we describe stability selection supervised principal component analysis for radiomics data with right-censored survival outcomes. The proposed approach allows us to identify a set of stable features that are highly associated with the survival outcomes, control the per-family error rate, and predict the survival in a simple yet meaningful manner. We evaluate the performance of SSSuperPCA using simulations and real data sets for non-small cell lung cancer and head and neck cancer, and compare it with other machine learning algorithms. The results demonstrate that our method has a competitive edge over other existing methods in identifying the prognostic markers from biomedical big imaging data for the prediction of right-censored survival outcomes. An R package SSSuperPCA is available at the website: http://web.hku.hk/∼herbpang/SSSuperPCA.html

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