On classification models of gene expression microarrays: The simpler the better

On classification models of gene expression microarrays: The simpler the better. Pranckeviciene, E. & Somorjai, R. In Proceedings of International Joint Conference on Neural Networks, 2006. IJCNN'06. July 16-21, Vancouver, Canada, pages 3572–3579, 2006. IEEE.

Link

On classification models of gene expression microarrays: The simpler the better [pdf]

Paper abstract bibtex

We investigate the relative efficacy of several classification models with and without feature selection. Simple classification rules are frequently preferable and superior to more complex models for microarray data that are typically undersampled. Improved classification accuracy is obtained with feature selection. We summarize some of the important questions considered in the literature that practitioners have to take into account when selecting a classifier for microarrays.

@inproceedings{pranckeviciene2006classification,
  title={On classification models of gene expression microarrays: The simpler the better},
  author={Pranckeviciene, Erinija and Somorjai, Ray},
  booktitle={Proceedings of International Joint Conference on Neural Networks, 2006. IJCNN'06. July 16-21, Vancouver, Canada},
  pages={3572--3579},
  year={2006},
  publisher={IEEE},
  abstract={We investigate the relative efficacy of several classification models with and without feature selection. Simple classification rules are frequently preferable and superior to more complex models for microarray data that are typically undersampled. Improved classification accuracy is obtained with feature selection. We summarize some of the important questions considered in the literature that practitioners have to take into account when selecting a classifier for microarrays.},
  keywords={gene expression, testing, biomedical informatics, sensitivity and specificity, machine learning, pattern recognition, data analysis},
url_Link={http://ieeexplore.ieee.org/document/1716589/},
url_Paper={http://erinijapranckeviciene.net/data_science_projects/IJCNN2006_Classification_GE.pdf}

}

Downloads: 0

{"_id":"Smo8CjWRk2bx8ukdm","bibbaseid":"pranckeviciene-somorjai-onclassificationmodelsofgeneexpressionmicroarraysthesimplerthebetter-2006","downloads":0,"creationDate":"2017-02-13T03:40:38.582Z","title":"On classification models of gene expression microarrays: The simpler the better","author_short":["Pranckeviciene, E.","Somorjai, R."],"year":2006,"bibtype":"inproceedings","biburl":"http://erinijapranckeviciene.net/erinija_publications.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"On classification models of gene expression microarrays: The simpler the better","author":[{"propositions":[],"lastnames":["Pranckeviciene"],"firstnames":["Erinija"],"suffixes":[]},{"propositions":[],"lastnames":["Somorjai"],"firstnames":["Ray"],"suffixes":[]}],"booktitle":"Proceedings of International Joint Conference on Neural Networks, 2006. IJCNN'06. July 16-21, Vancouver, Canada","pages":"3572–3579","year":"2006","publisher":"IEEE","abstract":"We investigate the relative efficacy of several classification models with and without feature selection. Simple classification rules are frequently preferable and superior to more complex models for microarray data that are typically undersampled. Improved classification accuracy is obtained with feature selection. We summarize some of the important questions considered in the literature that practitioners have to take into account when selecting a classifier for microarrays.","keywords":"gene expression, testing, biomedical informatics, sensitivity and specificity, machine learning, pattern recognition, data analysis","url_link":"http://ieeexplore.ieee.org/document/1716589/","url_paper":"http://erinijapranckeviciene.net/data_science_projects/IJCNN2006_Classification_GE.pdf","bibtex":"@inproceedings{pranckeviciene2006classification,\r\n title={On classification models of gene expression microarrays: The simpler the better},\r\n author={Pranckeviciene, Erinija and Somorjai, Ray},\r\n booktitle={Proceedings of International Joint Conference on Neural Networks, 2006. IJCNN'06. July 16-21, Vancouver, Canada},\r\n pages={3572--3579},\r\n year={2006},\r\n publisher={IEEE},\r\n abstract={We investigate the relative efficacy of several classification models with and without feature selection. Simple classification rules are frequently preferable and superior to more complex models for microarray data that are typically undersampled. Improved classification accuracy is obtained with feature selection. We summarize some of the important questions considered in the literature that practitioners have to take into account when selecting a classifier for microarrays.},\r\n keywords={gene expression, testing, biomedical informatics, sensitivity and specificity, machine learning, pattern recognition, data analysis},\r\nurl_Link={http://ieeexplore.ieee.org/document/1716589/},\r\nurl_Paper={http://erinijapranckeviciene.net/data_science_projects/IJCNN2006_Classification_GE.pdf}\r\n\r\n}\r\n\r\n\r\n","author_short":["Pranckeviciene, E.","Somorjai, R."],"key":"pranckeviciene2006classification","id":"pranckeviciene2006classification","bibbaseid":"pranckeviciene-somorjai-onclassificationmodelsofgeneexpressionmicroarraysthesimplerthebetter-2006","role":"author","urls":{" link":"http://ieeexplore.ieee.org/document/1716589/"," paper":"http://erinijapranckeviciene.net/data_science_projects/IJCNN2006_Classification_GE.pdf"},"keyword":["gene expression","testing","biomedical informatics","sensitivity and specificity","machine learning","pattern recognition","data analysis"],"downloads":0,"html":""},"search_terms":["classification","models","gene","expression","microarrays","simpler","better","pranckeviciene","somorjai"],"keywords":["gene expression","testing","biomedical informatics","sensitivity and specificity","machine learning","pattern recognition","data analysis"],"authorIDs":[],"dataSources":["wyhwdvMCTbALq9k63"]}