Methods and principles of selecting a subset of attributes for use in further modelling
12
votes
3answers
1k views
Variables are often adjusted (e.g. standardised) before making a model - when is this a good idea, and when is it a bad one?
In what circumstances would you want to, or not want to scale or standardize a variable prior to model fitting? And what are the advantages / disadvantages of scaling a variable?
23
votes
6answers
2k views
Feature selection for “final” model when performing cross-validation in machine learning
I am getting a bit confused about feature selection and machine learning
and I was wondering if you could help me out. I have a microarray dataset that is
classified into two groups and has 1000s of ...
8
votes
3answers
1k views
Feature selection and cross-validation
I have recently been reading a lot on this site (@Aniko, @Dikran Marsupial, @Erik) and elsewhere about the problem of overfitting occuring with cross validation - (Smialowski et al 2010 ...
9
votes
2answers
2k views
How to deal with collinearity issue when performing variable selection?
I've got a dataset with 9 continuous independent variables that I'm trying to select between to fit a model to a single percentage (dependent) variable, Score.
...
12
votes
6answers
1k views
Variable selection procedure for binary classification
What are the variable/feature selection that you prefer for binary classification when there are many more variables/feature than observations in the learning set? The aim here is to discuss what is ...
4
votes
5answers
369 views
Is using the same data for feature selection and cross-validation biased or not?
We have a small dataset (about 250 samples * 100 features) on which we want to build a binary classifier after selecting the best feature subset. Lets say that we partition the data into:
Training, ...
15
votes
5answers
931 views
Detecting significant predictors out of 300 independent variables
In a dataset of two non-overlapping populations (patients & healthy, total $n=60$) I would like to find (out of $300$ independent variables) significant predictors for a continuous dependent ...
12
votes
3answers
816 views
Why is variable selection necessary?
Common data-based variable selection procedures (for example, forward, backward, stepwise, all subsets) tend to yield models with undesirable properties, including:
Coefficients biased away from ...
12
votes
2answers
887 views
Significance testing or cross validation?
Two common approaches for selecting correlated variables are significance tests and cross validation. What problem does each try to solve and when would I prefer one over the other?
9
votes
4answers
760 views
Application of machine learning techniques in small sample clinical studies
What do you think about applying machine learning techniques, like Random Forests or penalized regression (with L1 or L2 penalty, or a combination thereof) in small sample clinical studies when the ...
5
votes
2answers
130 views
What can cause PCA to worsen results of a classifier?
I have a classifier that I'm doing cross-validation on, along with a hundred or so features that I'm doing forward selection on to find optimal combinations of features. I also compare this against ...
6
votes
4answers
226 views
Features for time series classification
I consider the problem of (multiclass) classification based on time series of variable length $T$, that is, to find a function
$$f(X_T) = y \in [1..K]\\
\text{for } X_T = (x_1, \dots, x_T)\\
...
2
votes
3answers
774 views
Logistic regression performance with high number of predictors
I'm trying to understand the behavior of logistic regression in high dimensional problems (i.e. when you are fitting a logistic regression to data with a high number of predictor variables).
Every ...
2
votes
2answers
372 views
Variables importance: who can do the most pushups?
I don't know enough math to formulate an intelligent question on this so I'll give an example. I'd like an answer to my example but also I'd like to know the jargon I need to be able to research it ...
5
votes
3answers
161 views
Can I perform an exhaustive search with cross-validation for feature selection?
I have been reading some of the posts about feature selection and cross-validation but I still have questions about the correct procedure.
Suppose I have a dataset with 10 features and I want to ...
3
votes
1answer
392 views
Feature selection methods for document classtification
I have a simple document classification problem where i need to classify some documents to a definite set of classes.
I need to perform a feature selection (where I will select the most important ...
3
votes
1answer
286 views
How can I assess how descriptive feature vectors are?
I am assessing how good different features are for unsupervised classification of a set of objects. For each different feature I test, I have computed a feature vector that describes the object. I ...
2
votes
2answers
56 views
Random search for the optimal number of input features and optimal number of hidden layers for a MLP?
I've preformed a random search in the hypothesis space $$\{(c,h)| c \in U[1,256]; h\in U[1,100];c \in \mathrm{Z} \text{ and } h \in \mathrm{Z}\}$$ that defines the parameters of a standard MLP neural ...
8
votes
3answers
841 views
The use of median polish for feature selection
In a paper I was reading recently I came across the following bit in their data analysis section:
The data table was then split into tissues and cell lines, and the two subtables were separately ...
1
vote
1answer
146 views
Issues with feature selection in matlab
I am trying to use sequentialfs to do some feature selection in matlab. I have huge dimensional data of 22215 features. When I tried to use sequentialfs with svm as classifier so that it selects the ...
1
vote
1answer
81 views
Selecting optimal number of input features and optimal number of hidden layers for a MLP?
What is the best way to select parameters for a binary neural network classifier? More specifically I have 265 features ranked according to Mutual Information Criterion. I have to determine the ...
0
votes
3answers
524 views
How to identify suitable variables to assess confounding, mediation and effect modification?
Imagine that you are planning a study about risk behaviours among HIV positive injecting drug users.
All the individuals included in the sample are injecting drugs and all are HIV positive. The main ...
