Methods and principles of selecting a subset of attributes for use in further modelling

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8
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3answers
1k 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 ...
5
votes
2answers
945 views

Is it possible to use kernel PCA for feature selection?

Is it possible to use kernel principal component analysis (kPCA) for Latent Semantic Indexing (LSI) in the same way as PCA is used? I perform LSI in R using the prcomp PCA function and extract the ...
20
votes
3answers
4k views

What are disadvantages of using the lasso for variable selection for regression?

From what I know, using lasso for variable selection handles the problem of correlated inputs. Also, since it is equivalent to Least Angle Regression, it is not slow computationally. However, many ...
9
votes
2answers
586 views

Best methods of feature selection for nonparametric regression

A newbie question here. I am currently performing a nonparametric regression using the np package in R. I have 7 features and using a brute force approach I identified the best 3. But, soon I will ...
4
votes
4answers
146 views

Feature selection for classification, controlling for sub-population

I have a bunch of points that belong to one of population P1, P2, ... Pn AND to class A or B. Within each population I'll be doing classification between A and B, and I want to select features that ...
8
votes
1answer
194 views

How to quantify redundancy of features?

I have three features that I use to solve a classification problem. Originally, these features produced boolean values, so I could evaluate their redundancy by looking at how much the sets of positive ...
2
votes
0answers
160 views

What should I do to compare different sets of data?

I am a beginner in statistics, and I want to learn machine learning :). Therefore, I have gathered some sample data to practice. But, the problem is I want to create a feature (or attribute), which is ...
0
votes
0answers
218 views

What are the most relevant metrics for social games, and how are they calculated?

For example some common ones are engagement, churn, ARPU. The problem with the metrics I mentioned (besides needen to know more) is that I do not understand clearly how are they measure and what they ...
9
votes
3answers
3k views

How to apply LASSO to IRLS (logistic regression)?

I have programmed a logistic regression using the IRLS algorithm. I would like to apply a LASSO penalization in order to automatically select the right features. At each iteration, the following is ...
2
votes
1answer
66 views

Analysis of variables of varying numbers

I work with amino acid sequences and I want to use a self-made model to tell me something about it, lets call it $f(\text{seq})$. Now i want to know the contribution of every position in the sequence ...
8
votes
2answers
2k views

Soft-thresholding vs. Lasso penalization

I am trying to summarize what I understood so far in penalized multivariate analysis with high-dimensional data sets, and I still struggle through getting a proper definition of soft-thresholding vs. ...
1
vote
2answers
213 views

How best to reduce dimensionality of a dataset composed of events and trials?

I'm trying to reduce dataset dimensionality. PCA is a good metric but that gives me new dataset. My goal is to determine from number of events (e.g. 60) and number of trials (e.g. 6) which events are ...
30
votes
8answers
4k 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 ...
18
votes
2answers
3k views

Variable importance from SVM

How to obtain a variable (attribute) importance using SVM?
7
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3answers
1k views

Computing best subset of predictors for linear regression

For the selection of predictors in multivariate linear regression with $p$ suitable predictors, what methods are available to find an 'optimal' subset of the predictors without explicitly testing all ...
10
votes
4answers
1k 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 ...
-1
votes
1answer
248 views

Shall I trust AIC (non-full model) or slope (full model)?

The purpose to run regressions for butterfly richness again 5 environmental variables is to show the importance rank of the independent variables mainly by AIC. In non-full models, they reveal that ...
18
votes
6answers
3k 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 ...
7
votes
6answers
2k views

Algorithms and methods for attribute/feature selection?

I have data with continuous class and I'm searching for good methods to reduce number of attributes. Now I'm using correlation based filters, random forests and Gram–Schmidt algorithm. What I want ...