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Lab Report: Intensity v Distance

door John Rodgers

TODOs 253

door Gary Sibbitts

Psychological Assessment

door Jouella Janine Esler

MAT.105X 9.2

door David Kedrowski

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