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

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2
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0answers
333 views

LASSO vs AIC for feature selection with the Cox model

I have some questions about the Lasso. After using the AIC or BIC to select a model, the model is fit with the variables selected in order to get the standard errors of the estimates with CIs, ...
1
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1answer
208 views

'Forward Stagewise' option in LARS algorithm

Can anyone help me understand the forward stagewise part in the LARS algorithm? I was reading the R code and could not figure out what is ...
2
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3answers
8k views

How should I interpret the p-values (i.e. t-tests) in regressions, and can I use them for feature selection?

I'm trying to do an OLS regression with several independent variables, and want to better understand how to interpret the p-values from doing the t-tests on the independent variables within my ...
3
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4answers
2k views

Decision Tree as variable selection for Logistic Regression

I have to do a Logistic Regression, and have to use a subset of the variables. I received this "tip": do a Decision Tree first, and use the most relevant variables in the Logistic Regression. Is this ...
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1answer
177 views

Random forest like procedure for regression or other statistical models

I'm wondering if there exist methods similar to one used in random forest algorithm - I mean taking simultaneously bootstrap sample and random subset of features, then building statistisal model. Have ...
2
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1answer
2k views

Feature selection using caret + repeatedcv

I am using caret and repeatedcv with repeats for feature selection. That is, ...
1
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0answers
691 views

How to select the best variables by RandomForest in R?

I have a table of mRNA levels of my target gene and it's transcription factors in many different condition. What I want to do is to select the most important ...
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0answers
332 views

Bootstrap randomized Lasso selection for a Cox model

I'm interested in variable selection for a cox proportional hazards model. I've read this article which slightly favours randomized bootstrap lasso selection over bootstrap lasso selection since it ...
3
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1answer
5k views

What is “feature space”?

What is the definition of "feature space"? For example, When reading about SVMs, I read about "mapping to feature space". When reading about CART, I read about "partitioning to feature space". I ...
1
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1answer
260 views

How to define in R the most important variables?

I have a data set with my target gene and more than thousand transcriptional factors somehow correlated with this gene. There is data of these factors in more than 70 variable conditions. What I'm ...
4
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0answers
349 views

time series with different length: feature extraction and classification [closed]

I have a binary classification problem, where each data point is multi-channel time-series, which can be represented as a matrix $T \times F$, where $T$ is the time-series length, and $F$ as the ...
3
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0answers
201 views

Fast algorithm for variable selection

The (training) data contains 1280 observations with 1415 features. The test set has additional 380 observations. The data is sparse, that is, many of the variables has many zeros and few positive ...
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0answers
255 views

Univariate feature ranking in classification

Scikit-learn has function to evaluate the F-statistics for univariate feature importance feature selection. According to the web page they are calculating ANOVA F value. If I understood correctly, ...
2
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1answer
182 views

Determining conserved features using a Bayesian approach

I would like to perform some sort of binary classification, and my data set consists of 100 examples (for each class), which are vectors with 2500 elements. Ideally, I would like to determine which ...
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0answers
409 views

Feature importance

The extremely randomized trees classifier (scikitlearn) provides a (multivariate) feature importance measurement Ensemble methods/feature importance evaluation. For each feature, the classifier ...
2
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0answers
323 views

Kernel in PenalizedSVM R package

There is not option to select kernel in penalizedSVM R package. What kernel do they use? Is there some other R package with penalized SVM methods where I can choose various kernels?
3
votes
1answer
65 views

Methods for teasing apart the influence of different time series features on a target feature?

Are there any established methods for teasing apart the influence of different time series features on a target feature? To illustrate: The target: Sales volume of kittens. Features: Time of year, ...
1
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1answer
294 views

When is the PMI value good or bad?

Pointwise mutual information is calculated by this formula $pmi(x;y) = log(p(x,y)/p(x)p(y))$ , my question now is, When is this pmi good and when is it bad. I know if the value is low it is bad, but ...
2
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1answer
286 views

Multiclass classification with SVM a question about the feature vectors

I was told I had to direct my machine learning questions to this site. So here it goes. I'm trying to do Multiclass classification with SVM. I have 7 classes. Now I was wondering if the following is ...
3
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1answer
82 views

Changing variable values and examine the outcome difference between the altered and original data

I recently read an approach which is used to find the effect of changing an independent variable. They are doing a classification problem, so each data row (or record) is associated with an outcome ...
-1
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1answer
134 views

How logical to select features with respect to the correlation matrix and weigthing?

Is it logical to name low correlated features as valuable and choosing the low corelated ones for classification? Or it depends on the algorithm used for the purpose? How do I need to interpret a ...
1
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0answers
90 views

Overfitting a linear Linear Discriminant Function

I am estimating a Linear Discriminant function with 250 input variables over 4000 data records. Should I consider feature selection, am I over fitting the model? How do I know when feature selection ...
4
votes
2answers
131 views

Random search for the optimal number of input features and optimal number of hidden layers for a MLP?

I've performed a random search in 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 multilayer ...
2
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1answer
247 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 ...
2
votes
2answers
104 views

Can feature selection be considered a way to observe relationship between variables like correlation?

In correlation we can observe relationship between a pair of variables, let me call it X1 and Y. Now, considering I have the predicting variables X1, X2, ..., Xn and the variable Y. Does the ...
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1answer
255 views

highly correlated features and high ranking

I am classifying different texts and I wondering about some features that are highly correlated. I have 49 features. Some features are absolute counters (integers) but most features are relative ...
3
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1answer
362 views

Why can't Bayesian variable selection be used with categorical variables with more than 2 levels?

I am reading this article which is the first approach on Bayesian variable selection. In the discussion section it says that one of the major limitations of the particular method is that it cannot be ...
5
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2answers
529 views

Why does increasing the number of features reduce performance?

I'm trying to gain an intuition as to why increasing the number of features could reduce performance. I'm currently using an LDA classifier which performs better bivariately among certain features ...
3
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0answers
205 views

Sensible to include ratio as a variable in logistic regression?

I'm creating a generalised linear regression using a binomial link function for two variables A and B. From looking at the data it appears that A/B may have discriminatory effect. Is it sensible to ...
6
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2answers
755 views

Fisher Distance for feature selection

I'm currently working for EEG signal classification from 3 electrodes. I want to have a simple feature selection algorithm that is independent with the classification process. From the feature ...
4
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2answers
3k views

Number of trees for Random Forest optimization using recursive feature elimination

How many trees would you suggest to pick to perform recursive feature elimination (RFE) in order to optimize Random Forest classifier (for binary classification problem). My dataset is very ...
0
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0answers
209 views

What are the good algorithms for feature extraction for large dataset?

I have KDD dataset for detecting fraud actions on networks but it has millions of lines and >20 feature columns. Thus it is not viable to process all these on my personal computer. I am thinking about ...
6
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5answers
2k 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, ...
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0answers
309 views

Adaboost feature weight calculation

I thought I understood Adaboost, until code analysis made me realize that sample_weight is not an array of the feature weights... and after further investigation I am left confused as to how ...
7
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3answers
13k views

How does one interpret SVM feature weights?

I am trying to interpret the variable weights given by fitting a linear SVM. (I'm using scikit-learn): ...
3
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2answers
428 views

How to do feature selection for learning from positive and unlabeled examples?

I have a binary classification task for German webpages for which I only have positive examples. That is why I use learning from positive and unlabeled examples as described on this page, also known ...
3
votes
3answers
2k views

How to reduce the number of variables in cluster analysis?

I've got 10 (yes, only 10) cases over 1000 variables (e.g. measurements of concentrations of 1000 different compounds at 10 different time points). I can group these cases into 3 clusters in ...
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2answers
812 views

Clustering time series with wavelets in R

Can discrete wavelet trasform be used for feature extraction from time series in order to cluster them? Any R code how to do this will be appreciated.
2
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0answers
127 views

Random forest like techniques (bagging, random feature subset) for SGD methods

Are there any well-known results/tools/literature on using bagging and random feature subset selection for regression or SGD-based methods?
5
votes
6answers
885 views

What machine learning algorithms are good for estimating which features are more important?

I have data with a minimum number of features that don't change, and a few additional features that can change and have a big impact on the outcome. My data-set looks like this: Features are A, B, C ...
6
votes
1answer
1k views

If p > n, the lasso selects at most n variables

One of the motivations for the elastic net was the following limitation of LASSO: "In the p > n case, the lasso selects at most n variables before it saturates, because of the nature of the convex ...
8
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4answers
3k views

Low classification accuracy, what to do next?

So, I'm a newbie in ML field and I try to do some classification. My goal is to predict the outcome of a sport event. I've gathered some historical data and now try to train a classifier. I got around ...
5
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3answers
583 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 ...
2
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1answer
443 views

Advice for a sparse high-dimensional regression strategy

I have a regression problem where I would like to predict values given several thousand sparse features. The general data set is an $n \times m$ matrix where each row contains a sample with a value I ...
2
votes
1answer
150 views

What properties of a text makes it a spam/bad question?

I'm trying to identify numeric properties of a text message that make it a spam or, more specifically, a bad question on sites like this one. For example, would things like capital letter density ...
0
votes
1answer
187 views

R package for feature set algorithm selection

I want to train a binary classification NN and part of this will require data pre-processing. However, I have a choice of which pre-processing algorithm to use. Of course I'd like to choose that one ...
1
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0answers
400 views

Feature selection for SVM and Maximum Entropy

In text classification problems where the number of features >> number of documents, is it useful to perform feature selection with filters (e.g. Information Gain) when using Naive Bayes. However, ...
4
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0answers
172 views

Variable Selection One by One vs Simultaneously

The high dimensional variable selection problem is really popular now. But I have a question: If I do simple linear regression regressing one response variable on 1 covariate at a time first and then ...
8
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3answers
2k views

Feature selection using mutual information in Matlab

I am trying to apply the idea of mutual information to feature selection, as described in these lecture notes (on page 5). My platform is Matlab. One problem I find when computing mutual information ...
6
votes
1answer
2k views

Feature selection and parameter tuning with caret for random forest

I have data with a few thousand features and I want to do recursive feature selection (RFE) to remove uninformative ones. I do this with caret and RFE. However, I started thinking, if I want to get ...