Evaluating Statistical and Machine Learning Supervised Classification Methods

2018 ◽  
pp. 37-53 ◽  
Author(s):  
David J. Hand
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Classification Methods ◽  
Supervised Classification Methods

Comparison of Supervised Classification Methods for Protein Profiling in Cancer Diagnosis

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Nadège Dossat ◽  
Alain Mangé ◽  
Jérôme Solassol ◽  
William Jacot ◽  
Ludovic Lhermitte ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Supervised Classification ◽  
Protein Profiling ◽  
Clinical Proteomics ◽  
High Dimensional ◽  
Classification Methods ◽  
Linear Discriminant ◽  
Supervised Classification Methods

A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The first two steps are the selection of the most discriminating biomarkers with a construction of different classifiers. Finally, we compare and validate their performance and robustness using different supervised classification methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classification Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested.

A Comparison of Semi-Supervised Classification Approaches for Software Defect Prediction

2014 ◽  
Vol 23 (1) ◽  
pp. 75-82 ◽  
Author(s):  
Cagatay Catal
Keyword(s):  
Supervised Classification ◽  
Defect Prediction ◽  
Support Vector ◽  
Software Defect Prediction ◽  
Classification Methods ◽  
Data Set ◽  
Software Defect ◽  
Data Points ◽  
Supervised Classification Methods ◽  
Prediction Approach

AbstractPredicting the defect-prone modules when the previous defect labels of modules are limited is a challenging problem encountered in the software industry. Supervised classification approaches cannot build high-performance prediction models with few defect data, leading to the need for new methods, techniques, and tools. One solution is to combine labeled data points with unlabeled data points during learning phase. Semi-supervised classification methods use not only labeled data points but also unlabeled ones to improve the generalization capability. In this study, we evaluated four semi-supervised classification methods for semi-supervised defect prediction. Low-density separation (LDS), support vector machine (SVM), expectation-maximization (EM-SEMI), and class mass normalization (CMN) methods have been investigated on NASA data sets, which are CM1, KC1, KC2, and PC1. Experimental results showed that SVM and LDS algorithms outperform CMN and EM-SEMI algorithms. In addition, LDS algorithm performs much better than SVM when the data set is large. In this study, the LDS-based prediction approach is suggested for software defect prediction when there are limited fault data.

scholarly journals Twelve Numerical, Symbolic and Hybrid Supervised Classification Methods

1998 ◽  
Vol 12 (05) ◽  
pp. 517-571 ◽  
Author(s):  
Olivier Gascuel ◽  
Bernadette Bouchon-Meunier ◽  
Gilles Caraux ◽  
Patrick Gallinari ◽  
Alain Guénoche ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Hybrid Methods ◽  
Common Factor ◽  
Rule Based ◽  
Practical Applications ◽  
Hybrid Approaches ◽  
The Subject ◽  
Supervised Classification Methods ◽  
Representation Of Knowledge

Supervised classification has already been the subject of numerous studies in the fields of Statistics, Pattern Recognition and Artificial Intelligence under various appellations which include discriminant analysis, discrimination and concept learning. Many practical applications relating to this field have been developed. New methods have appeared in recent years, due to developments concerning Neural Networks and Machine Learning. These "hybrid" approaches share one common factor in that they combine symbolic and numerical aspects. The former are characterized by the representation of knowledge, the latter by the introduction of frequencies and probabilistic criteria. In the present study, we shall present a certain number of hybrid methods, conceived (or improved) by members of the SYMENU research group. These methods issue mainly from Machine Learning and from research on Classification Trees done in Statistics, and they may also be qualified as "rule-based". They shall be compared with other more classical approaches. This comparison will be based on a detailed description of each of the twelve methods envisaged, and on the results obtained concerning the "Waveform Recognition Problem" proposed by Breiman et al.,4 which is difficult for rule based approaches.

Sign in / Sign up

Export Citation Format

Share Document