A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data
المؤلفون المشاركون
Bommert, Andrea
Lang, Michel
Rahnenführer, Jörg
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-18، 18ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-08-01
دولة النشر
مصر
عدد الصفحات
18
التخصصات الرئيسية
الملخص EN
Finding a good predictive model for a high-dimensional data set can be challenging.
For genetic data, it is not only important to find a model with high predictive accuracy, but it is also important that this model uses only few features and that the selection of these features is stable.
This is because, in bioinformatics, the models are used not only for prediction but also for drawing biological conclusions which makes the interpretability and reliability of the model crucial.
We suggest using three target criteria when fitting a predictive model to a high-dimensional data set: the classification accuracy, the stability of the feature selection, and the number of chosen features.
As it is unclear which measure is best for evaluating the stability, we first compare a variety of stability measures.
We conclude that the Pearson correlation has the best theoretical and empirical properties.
Also, we find that for the stability assessment behaviour it is most important that a measure contains a correction for chance or large numbers of chosen features.
Then, we analyse Pareto fronts and conclude that it is possible to find models with a stable selection of few features without losing much predictive accuracy.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Bommert, Andrea& Rahnenführer, Jörg& Lang, Michel. 2017. A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-18.
https://search.emarefa.net/detail/BIM-1142328
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Bommert, Andrea…[et al.]. A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-18.
https://search.emarefa.net/detail/BIM-1142328
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Bommert, Andrea& Rahnenführer, Jörg& Lang, Michel. A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-18.
https://search.emarefa.net/detail/BIM-1142328
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1142328
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر