Dataminnig

Features Selection and Extraction Margot Cuar´n a

Features Selection and Extraction
Margot Cuar´n a

September 29, 2009

Margot Cuar´n a

Features Selection and Extraction

Outline
Features Selection and Extraction Margot Cuar´n a

1 Introduction

Margot Cuar´n a

Features Selection and Extraction

Outline
Features Selection and Extraction Margot Cuar´n a

1 Introduction2 KDD

Data mining
Challengers

Data Preprocessing
Data Reduction

Margot Cuar´n a

Features Selection and Extraction

Outline
Features Selection and Extraction Margot Cuar´n a

1 Introduction 2 KDD

Data mining
Challengers

Data Preprocessing
Data Reduction

3 Features

Margot Cuar´n a

Features Selection and Extraction

Outline
Features Selection and ExtractionMargot Cuar´n a

1 Introduction 2 KDD

Data mining
Challengers

Data Preprocessing
Data Reduction

3 Features 4 Feature Selection and Extraction

Margot Cuar´n a

Features Selection and Extraction

Introduction
Features Selection and Extraction Margot Cuar´n a Introduction KDD Data mining Challengers Data Preprocessing Data Reduction Features Feature Selection and Extraction

Inrecent years many applications of data mining (text mining, bioinformatics, sensor networks) deal with a very large number n of features (e.g. tens or hundreds of thousands of variables) and often comparably few samples. In these cases, it is common practice to adopt feature selection algorithms to improve the generalization accuracy. There are many potential benefits of feature selection:
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Facilitating data visualization and data understanding, Reducing the measurement and storage requirements, Reducing training and utilization times, Defying the curse of dimensionality to improve prediction performance.

Margot Cuar´n a

Features Selection and Extraction

Introduction
Features Selection and Extraction Margot Cuar´n a Introduction KDD Data mining Challengers DataPreprocessing Data Reduction Features Feature Selection and Extraction

The availability of massive amounts of experimental data based on genome-wide studies has given impetus in recent years to a large effort in developing mathematical, statistical and computational techniques to infer biological models from data. In many bioinformatics problems the number of features is significantly larger than thenumber of samples (high feature to sample ratio datasets).

Margot Cuar´n a

Features Selection and Extraction

Goals
Features Selection and Extraction Margot Cuar´n a Introduction KDD Data mining Challengers Data Preprocessing Data Reduction Features Feature Selection and Extraction

What is Feature Selection for KDD? Why feature selection is important? What is the filter and what is thewrapper approach to feature selection? Examples

Margot Cuar´n a

Features Selection and Extraction

Knowledge Discovery in Databases (KDD)
Features Selection and Extraction Margot Cuar´n a Introduction KDD Data mining Challengers Data Preprocessing Data Reduction Features Feature Selection and Extraction

Definition The non-trivial extraction of implicit, unknown, and potentially usefulinformation from data.

Margot Cuar´n a

Features Selection and Extraction

Outline
Features Selection and Extraction Margot Cuar´n a

1 Introduction
Introduction KDD Data mining Challengers Data Preprocessing Data Reduction Features Feature Selection and Extraction

2 KDD

Data mining
Challengers

Data Preprocessing
Data Reduction

3 Features 4 Feature Selection andExtraction

Margot Cuar´n a

Features Selection and Extraction

Data mining
Features Selection and Extraction Margot Cuar´n a Introduction KDD Data mining Challengers Data Preprocessing Data Reduction Features Feature Selection and Extraction

Definition Data mining is the process of selecting, exploring, and modeling large amounts of data to uncover previously unknown patterns for business...