Tio

Just wanna share my experience

Classification using L1-Penalized Logistic Regression


There are various classification algorithms that have been developed in different fields. Some algorithms are commonly used in genomics such as linear discriminant analysis (LDA), nearest neighbor classifier and logistic regression. Many authors such as Gohlmann and Talloen (2009), and Lee (2005) have comprehensively reviewed and compared of these algorithms.
Logistic regression is a supervised method for binary or multi-class classification (Hosmer and Lemeshow 1989). Because it is a simple, flexible and straightforward model that is easy to extend, the extensions of logistic regression
have been widely used in genomics research (e.g., Liao and Chin, 2007, and Sun and Wang, 2012).
In high-dimensional datasets such as in microarray settings where usually there are more variables than the observations and variables are correlated (multicolinierity), the classical logistic regression would perform badly and provide inaccurate estimates. It would give a perfect fit to the data with no bias and high variance which can lead to bad prediction (overfitting). In order to prevent this problem, a penalty for complexity in the model should be introduced.

The presentation which can be viewed here shows a short overview of L1 penalization logistics regression.  Example of the application of this method in genomic is to define  candidate classifiers genes to classify two different groups, e.g., cancer and non-cancer group.

References 

• Lee JW, et al, 2005. An extensive comparison of recent classification tools applied to microarray data. Computational Statistics & Data Analysis. 48:869-885.
• Hosmer, D.W., Lemeshow, S., 1989. Applied Logistic Regression. Wiley Series in Probability and Mathematical Statistics. Wiley, New York, NY.
• Sun, H. andWang, S. 2012. Penalized logistic regression for high-dimensional DNA methylation data with case-control studies Bioinformatics. 28(10):1368-1375
• Tibshirani, R. 1996. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society Series B (Methodological). 58:267- 288.
• Goeman, J.J. 2010. L1 Penalized Estimation in the Cox Proportional Hazards Model. Biometrical Journal. 52 (1): 70-84.
• Gohlmann, H., and, Talloen, W. 2009. Gene Expression Studies Using Affymetrix Microarrays. Chapman & Hall/CRC.
• Liao, J.G. , and Chin, K.V. 2007. Logistic regression for disease classification using microarray data: model

October 7, 2012 - Posted by | Bioinformatics, education, Statistics | , , , , , ,

6 Comments »

  1. wah, akhirnya saya bisa menemukan blog yang mengulas tentang penalized logistic regression. Kebetulan saya juga sedang mengerjakan skripsi tentang penalized logistic, tetapi dalam jangkauan ilmu yang masih dasar karena menggunakan pendekatan firth’s procedure. Oh iya, saya ingin bertanya apakah di Belgia atau Swedia ada program Master by Coursework seperti di Australia?.
    Terima Kasih.

    Comment by eve | October 9, 2012 | Reply

    • kebanyakan disini masternya by coursework

      Comment by Tio | October 13, 2012 | Reply

  2. Terima kasih atas infonya. Oh iya, kalo saya boleh tahun deadline pendaftaran untuk universitas yang ada di belgia biasany pada bulan apa saja?, terima kasih.

    Comment by vellinlusiana | December 11, 2012 | Reply

    • biasanya sampe maret utk non EU

      Comment by Tio | December 11, 2012 | Reply

  3. Oh, begitu. Baik terima kasih. Saya ingin mencoba apply S2 di Belgia begitu saya lulus. Apakah ada persyaratan mengenai biaya saat apply?, maksud saya apakah kita harus menyertakan keterangan bank deposit atau semacamnya?. Terima kasih.

    Comment by vellinlusiana | December 11, 2012 | Reply

    • Untuk self funding ada ketentuan untuk jumlah deposit, ini bisa ditanyakan ke belgium embassy di jakarta. Untuk beasiswa tidak diperlukan deposit krn lembaga beasiswa yg akan menanggung living costnya.

      Comment by Tio | December 14, 2012 | Reply


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