Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm

QR Code

Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm

Gene expression data sets are used to classify and predict patient diagnostic categories. As we know, it is extremely difficult and expensive to obtain gene expression labelled examples. Moreover, conventional supervised approaches cannot function properly when labelled data (training examples) are...

Full description

Bibliographic Details
Main Authors:	Helmi, Hala, Garibaldi, Jonathan M., Aickelin, Uwe
Format:	Conference or Workshop Item
Published:	2011
Online Access:	https://eprints.nottingham.ac.uk/2024/

Similar Items

Efficient feature selection analysis for accuracy malware classification
by: Rahiwan Nazar, Romli, et al.
Published: (2021)

Games participants glad none was hurt
by: new straits times,
Published: (2009)

Gladly sweating it out for winter festival
by: Yang, Minnow
Published: (2019)

Researchers glad they can use medical cannabis
by: Mohd Said, Sakini
Published: (2021)

The evaluation of accuracy performance in an enhanced embedded feature selection for unstructured text classification
by: Nur Syafiqah, Mohd Nafis, et al.
Published: (2020)

ABC: android botnet classification using feature selection and classification algorithms
by: Abdullah, Zubaile, et al.
Published: (2017)

Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms
by: Rachmad, Iqbal, et al.
Published: (2024)

'Glad tidings, testimony and research': Sixty years of the Australian Library Journal
by: Haddow, Gaby
Published: (2011)

Genetic algorithm seeding of idiotypic networks for mobile-robot navigation
by: Whitbrook, Amanda, et al.
Published: (2008)

A comparison of non-stationary, type-2 and dual surface fuzzy control
by: Benatar, Naisan, et al.
Published: (2011)

RFE-based feature selection to improve classification accuracy for morphometric analysis of craniodental characters of house rats
by: Aneesha Balachandran Pillay,, et al.
Published: (2023)

PV fault classification: Impact on accuracy performance using feature extraction in random-forest cross validation algorithm
by: Muhamad Zahim, Sujod, et al.
Published: (2024)

Comparison of Recursive Feature Elimination and Boruta as Feature Selection in Greenhouse Gas Emission Data Classification
by: Riko, Febrian, et al.
Published: (2024)

Improved building roof type classification using correlation-based feature selection and gain ratio algorithms
by: Norman, M., et al.
Published: (2017)

Tuning a multiple classifier system for side effect discovery using genetic algorithms
by: Reps, Jenna M., et al.
Published: (2014)

Comparison of algorithms that detect drug side effects using electronic healthcare databases
by: Reps, Jenna M., et al.
Published: (2013)

Accuracy and performance analysis for classification algorithms based on biomedical datasets
by: Al-Hameli, Bassam Abdo, et al.
Published: (2021)

Feature selection based on particle swarm optimization algorithm for sentiment analysis classification
by: Nurcahyawati, Vivine, et al.
Published: (2021)

Classification Algorithms and Feature Selection Techniques for a Hybrid Diabetes Detection System
by: Al-Hameli, Bassam Abdo, et al.
Published: (2021)

Using feature selection as accuracy benchmarking in clinical data mining.
by: Hossain, Jafreen, et al.
Published: (2013)

Impact of feature extraction techniques on classification accuracy for EMG based ankle joint movements
by: Al-Quraishi, Maged Saleh Saeed, et al.
Published: (2015)

Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database
by: Liu, Yihui, et al.
Published: (2015)

Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database
by: Liu, Yihui, et al.
Published: (2014)

Feature selection for classification of hyperspectral data by SVM
by: Pal, Mahesh, et al.
Published: (2010)

A review of feature selection on text classification
by: Nur Syafiqah, Mohd Nafis, et al.
Published: (2018)

Spectral feature selection and classification of roofing materials using field spectroscopy data
by: Samsudin, Sarah Hanim, et al.
Published: (2015)

Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data
by: Liu, Yihui, et al.
Published: (2013)

Comparison of accuracy performance based on normalization techniques for the features fusion of face and online signature
by: Suryanti, Awang, et al.
Published: (2017)

Quiet in class: classification, noise and the dendritic cell algorithm
by: Gu, Feng, et al.
Published: (2011)

An Examination of feature selection frameworks in Text Categorization
by: Bong, Chih How, et al.
Published: (2005)

Improving sentiment classification accuracy of financial news using n-gram approach and feature weighting methods
by: Yazdani, Sepideh Foroozan, et al.
Published: (2015)

Real-world transfer of evolved artificial immune system behaviours between small and large scale robotic platforms
by: Whitbrook, Amanda, et al.
Published: (2010)

'Enhanced Direct and Indirect Genetic Algorithm Approaches for a Mall Layout and Tenant Selection Problem'
by: Aickelin, Uwe, et al.
Published: (2002)

Enhanced Direct and Indirect Genetic Algorithm Approaches for a Mall Layout and Tenant Selection Problem
by: Aickelin, Uwe, et al.
Published: (2002)

'Enhanced Direct and Indirect Genetic Algorithm Approaches for a Mall Layout and Tenant Selection Problem'
by: Aickelin, Uwe, et al.
Published: (2002)

The impact of pre-processing and feature selection on text classification
by: Suryanti, Awang, et al.
Published: (2020)

The DCA:SOMe comparison: a comparative study between two biologically-inspired algorithms
by: Greensmith, Julie, et al.
Published: (2008)

Nurse Rostering with Genetic Algorithms
by: Aickelin, Uwe
Published: (1998)

Harshness in image classification accuracy assessment
by: Foody, Giles M.
Published: (2008)

A comparison of three different methods for classification of breast cancer data
by: Soria, Daniele, et al.
Published: (2008)