'Feature subset' presentation slideshows

All classification methods we have studied so far use all genes/features

CZ5225: Modeling and Simulation in Biology Lecture 8: Microarray disease predictor-gene selection by feature selection methods Prof. Chen Yu Zong Tel: 6874-6877 Email: phacyz@nus.edu.sg http://bidd.nus.edu.sg Room 07-24, level 8, S17, National University of Singapore. Gene selection?.

538 views • 36 slides

Dimensionality Reduction by Feature Selection in Machine Learning

Dimensionality Reduction by Feature Selection in Machine Learning. Dunja Mladeni ? J.Stefan Institute, Slovenia. Reasons for dimensionality reduction. Dimensionality reduction in machine learning is usually performed to: Improve the prediction performance Improve learning efficiency

654 views • 35 slides

Genetic Feature Subset Selection for Gender Classification: A Comparison Study

Genetic Feature Subset Selection for Gender Classification: A Comparison Study. Zehang Sun, George Bebis, Xiaojing Yuan, and Sushil Louis Computer Vision Laboratory Department of Computer Science University of Nevada, Reno bebis@cs.unr.edu http://www.cs.unr.edu/CVL. Gender Classification.

345 views • 34 slides

Hold and Sign: A Novel Behavioral Biometrics for Smartphone User Authentication

Hold and Sign: A Novel Behavioral Biometrics for Smartphone User Authentication. Presented by: Dhruva Kumar Srinivasa Team-mate: Nagadeesh Nagaraja. Authors. Attaullah Buriro - Dept . of Inf. Eng. & Comput . Sci., Univ. of Trento, Trento, Italy

323 views • 32 slides

Sequential Genetic Search for Ensemble Feature Selection

303 views • 30 slides

513 views • 25 slides

Competitions in machine learning: the fun, the art, and the science Isabelle Guyon

Competitions in machine learning: the fun, the art, and the science Isabelle Guyon Clopinet, Berkeley, California http://clopinet.com/challenges isabelle@clopinet.com. My Itinerary. My Kids. My Company. Recent Projects. Drug toxicity via flow cytometry. Melanoma App.

255 views • 25 slides

IEEE IRI 2006, Waikoloa, Hawaii, USA, Sep 16-18, 2006.

Integrating Genetic Algorithms with Conditional Random Fields to Enhance Question Informer Prediction. Min-Yuh Day 1, 2 , Chun-Hung Lu 1, 2 , Chorng-Shyong Ong 2 , Shih-Hung Wu 3 , and Wen-Lian Hsu 1,* , Fellow, IEEE 1 Institute of Information Science, Academia Sinica, Taiwan

331 views • 23 slides

Feature Selection for Regression Problems

Feature Selection for Regression Problems. M. Karagiannopoulos, D. Anyfantis, S. B. Kotsiantis, P. E. Pintelas Educational Software Development Laboratory and Computers and Applications Laboratory Department of Mathematics, University of Patras, Greece. Scope.

428 views • 22 slides

COT6930 Course Project

COT6930 Course Project. Outline. Gene Selection Sequence Alignment. Why Gene Selection. Identify marker genes that characterize different tumor status. Many genes are redundant and will introduce noise that lower performance.

216 views • 21 slides

Machine Learning Feature Creation and Selection

Machine Learning Feature Creation and Selection. Feature creation. Well-conceived new features can sometimes capture the important information in a dataset much more effectively than the original features. Three general methodologies: Feature extraction

371 views • 18 slides

Dimensionality R e d u c t i o n

Dimensionality R e d u c t i o n. Another unsupervised task. Clustering, etc. -- all forms of data modeling Trying to identify statistically supportable patterns in data Another way of looking at it: reduce complexity of data Clustering: 1000 data points ? 3 clusters

309 views • 18 slides

Significance Test for Feature Selection on Image Recognition

Significance Test for Feature Selection on Image Recognition. Qianren Xu Mohamed Kamel Magdy M. A. Salama. Outline. Introduction Methodology Experiment Results Conclusion. Introduction. Problems on Features. Problems on Feature Selection Methods. Very large number Irrelevant Noise

266 views • 17 slides

Spanish Inquisition

Chris. Leon. Spanish Inquisition. Yan. Final Project Week 2 - 4/29/09 Breast Cancer Gene Expression Data Leon Kay, Yan Tran, Chris Thomas. Weka Filtering. Used CFS with BestFirst Search Reduced the number of attributes from 1544 to 125

572 views • 14 slides