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Kategoriak: All - features - classification - error - samples

arabera bosio mattia 12 years ago

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In the realm of microarray classification, the challenge of dealing with few samples compared to the vast number of genes is prevalent. Various filter approaches for univariate feature selection are often employed but risk overfitting to the training data.

Honelako gehiago

CNN_2

by Anand Puntambekar

What have I done on my PhD?

by Graeme Smith

DIPHTHONGS

by Erika De la Rosa

Application of Next Generation Sequencing (NGS)

by Rozaimi Razali

Computation Time

Tree construction time ?

Need to include that?

Feature selection

99% of time

Time scalability with features

IFFS SW : linear x Wsize

IFFS : non linear and unfeasible

SFFS : linear

Is our algorhtm better ?

Common Ground

MAQC II set

MCC value comparable

Many samples

Contemporary

YES for MCC mean vlue

MCC value Mean value across endpoints

Metagenes are useful?

Treelet/ Euclidean ?

Is the improvement enough to compensate for tree construcion?

PROS

-Resume of + genes

- Expanded feat space

- Interpretable comb.

-Common behaviour

NEW ELEMENT?

Reliability Score useful?

Score parameter

Include it in article?

Selection how?

Classifier Transparent

Any other, only needed dist from boundary

LDA

Interpretable

Robust

Simple

NEW ELEMENT!

More useful for small sample number After that is it totally related with error rate?

gives more info about data distribution wrt classifier boundary than ERROR RATE only

Microarray classification

overfit to train data Infer data distribution from train set

Many filter approaches problem of univariate feature selection

Few samples wrt gene number