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

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What are the disadvantages of using the Lasso for feature selection in classification (in comparison to a brute force)?

As far as I understand, feature selection is difficult for classification problems because it's effectively impossible to identify an optimal subset of $k$ features in problems where the the total ...
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1answer
100 views

Which feature selection method to use for classification problem

I have to do some feature selection for a classification problem with numeric features. I am not sure which feature selection method to use. Chisquared test or Spearmann's rank correlation ...
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12 views

Python Scikit-learn CountVectorizer throwing ValueError: empty vocabulary [on hold]

I'm trying to extract features from a text document. Here is my code: import sklearn from sklearn.datasets import load_files ...
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2answers
36 views

Feature selection before neural network classification

I have a training set of 87 samples and 9480 variables. My predictors are continuous and my response variable is binary. I'd like to use the caret package in R to tune a neural network classification ...
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1answer
89 views

How to handle missing data in a small $n$ large $k$ machine learning scenario?

I have a sample size $N=130$ and $1000$ variables. I am using machine learning techniques (SVM) for analysing the data. Some variables in the dataset have values that are so huge that they must be ...
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2answers
42 views

Which variables to keep in my analysis based on loadings from PCA? [duplicate]

Could someone please explain me how I should decide which variables to keep in my analysis based on loadings from PCA. The output is: ...
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0answers
19 views

How to reduce the number of features for Gaussian Process regression?

Ridge regression reduces complexity of the model by scaling down the coefficient. Lasso reduces the complexity of the model by selecting the features used. For Gaussian Process, is there similar way ...
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1answer
49 views

Quantitative importance for interacting variables in Artificial Neural Networks?

Is there any common/sound method to quantify (similar to T-test or F-test in regression models) the measures of influence and significance of terms in Artificial Neural Networks? By terms I mean both ...
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1answer
18 views

Alternative to AIC for feature selection in classification

I want to know what are the most common methods for feature selection in classification problems (binary and mutli-class). I see in Chapter 6 of Zumel and Mount that they use AIC before they train ...
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8 views

Akaike information criterion for categorical and numerical data

How should I compute AIC for categorical and for numeric variables in classification problems? I see in Chapter 6 of Zumel and Mount that they use AIC before they train classification algorithms ...
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3answers
1k views

How to use principal components as predictors in GLM?

How would I use the output of a principal components analysis (PCA) in a generalized linear model (GLM), assuming the PCA is used for variable selection for the GLM? Clarification: I want to use PCA ...
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1answer
35 views

Model Selection and RFE using caret

I'm faced with a high dimensional (samples=148, features=20000), supervised binary classification problem. Which I would like to approach with an ensemble of classifiers, that will classify using a ...
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12 views

R language: Can the function rfe of the package caret be used with a mixed effect model [migrated]

I would like to do feature selection with a mixed effect model in R, but I cannot manage to combine the function rfe of the package caret with the function me of ...
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19 views

How to find original features corresponding to the first two principal components? [duplicate]

I have a set of data described by $n$ features. I do a principal component analysis (PCA) to reduce it to just 2 dimensions so I can make a 2D plot of the data, with the first two Principal ...
3
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54 views

When should I use feature selection and when should I use dimensionality reduction techniques?

When should I use feature selection and dimensionality reduction? I know that feature selection is different from dimensionality reduction. But I don't know under what circumstances should I use ...
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40 views

In PCA, can the values in the principle component vectors which are close to zero be removed to see the important features? [duplicate]

In PCA, when I extract the principle component vectors, I am choosing the first vector with the largest corresponding eigenvalue. I notice that some of the values in this vector are close to zero. Can ...
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1answer
169 views

How to normalize time series?

This is a general question on normalization of data so that all the variables are within the same range. Why do we normalize data in pattern classification? How to normalize time series which is ...
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1answer
463 views

Issues with sequential feature selection

I am trying to do some feature selection in gene expression data with 22215 features. I followed the tutorial here. I initially applied filter method(ttest) to select the features having the best p ...
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17 views

To be significant or to be stabile, what's more scientfically important?

Recently I discovered the techniques related to cross validation. Basically you can split up your data in n groups and then run your model on one part of the data and assess prediction reliability on ...
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5 views

Theoretical Results for Feature Selection

I am looking for some papers that give theoretical results, such as bounds for PAC-learning or VC-dimension relationships, for instance space transformations due to feature selection. What are some ...
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3answers
873 views

Model stability when dealing with large $p$, small $n$ problem

Intro: I have a dataset with a classical "large p, small n problem". The number available samples n=150 while the number of possible predictors p=400. The outcome is a continuous variable. I want ...
2
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1answer
23 views

How to handle changing input vector length with neural networks

I want to train a neural network with a sequence of character as an input vector. Learning examples have different length and for this reason I don't know how to represent them. Let's say I have two ...
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2answers
1k 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 ...
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1answer
103 views

Feature selection and training on the same sample

Is feature selection and training on the same sample a bad idea? I want to emphasize that I am not going to use test set for feature selection. If I use the whole train set for feature selection and ...
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2answers
972 views

Variablity in cv.glmnet results

I am using cv.glment to find predictors. The set-up I use is as follows: ...
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1answer
27 views

Proper variable selection: Use only training data or full data?

I'm going through the lab exercises in "Introduction to Statistical Learning" and am having difficulty understanding the proper way to do best subset selection. The book is available here ...
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7 views

Is there an algorithm to determine if too few features are selected for k-nearest-neighbor?

Is there an algorithm to determine if too few features are selected for k-nearest-neighbor when no test set is available, --when the input vectors are unknowns? Here's my problem, I have a massive ...
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35 views

In recursive feature elimination in random forest, why are all features selected?

I am trying to use the recursive feature elimination in caret package. Here's the code; ...
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7 views

Elastic net is being used in genome wide analysis. Similar approach would work for survey analysis?

I'm approaching the elastic net procedure for genome wide analysis (GWAS) because it allows for feature selection, groups detection and improved validity. It's a powerful technique when you have many ...
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2answers
120 views

Extract important features

Here is my situation: - A huge amount of data - 600 features - Only one class is provided Now, my question is how can I reduce the number of features to important ones? In another word, all of these ...
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1answer
285 views

How to use rfe object with function pickSizeTolerance in R package caret

I run caret's recursive feature selection with randomForest. While running rfe function with method repeatedcv, I had parameter ...
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0answers
17 views

Can I reuse the dataset set aside for performing t-test based on the following condition?

I have a small number of samples and large number features. For doing the feature selection I'm going to divide my total set into a feature selection set and a test set.I run the t-test on the former ...
3
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1answer
69 views

What is the difference between feature selection and dimensionality reduction?

I know that both feature selection and dimensionality reduction aim towards reducing the number of features in the original set of features. What is the exact difference between the two if we are ...
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1answer
275 views

Random forest cross validation for feature selection, imbalanced datasets

I have an 5297X26 imbalanced dataset, the class1 has 588 samples and class2 has 4709 samples. I used the following code to perform random forest: ...
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2answers
172 views

Evaluating features and similarity measures

I am currently developing a classificator, which is supposed to classify into a number of classes. For this purpose I am designing some features and similarity measures which I might use for a later ...
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1answer
91 views

Feature selection for pattern mining

I must find frequent patterns in temporal data, using a method that was imposed to me. This tool has problems handling these data: processing is long and takes a lot of memory. So, I would like to ...
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1answer
1k views

Clustering probability distributions - methods & metrics?

I have some data points, each containing 5 vectors of agglomerated discrete results, each vector's results generated by a different distribution, (the specific kind of which I am not sure, my best ...
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2answers
3k views

Finding the best features in interaction models

I have list of proteins with their feature values. A sample table looks like this: ...
2
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1answer
170 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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19 views

SVM In text classifcation

I am learning about SVM in text classification. However, here i am posed with a problem. I have a dataset of documents which have 3 class labels. First Question Do i split the dataset into ...
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1answer
26 views

SVM Classifier: proper process

I'm working on a classification system of mine, but am needing help with the proper process order. Specifically, I'm using LibSVM and a range of feature sets extracted from my data. I'm wondering, ...
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5answers
3k views

Can I use PCA to do variable selection for cluster analysis?

I have to reduce the number of variables to conduct a cluster analysis. My variables are strongly correlated, so I thought to do a Factor Analysis PCA (principal component analysis). However, if I use ...
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1answer
43 views

determining how “important” a feature is in predicting a target in decision trees

Random forests allow us to compute a heuristic for determining how "important" a feature is in predicting a target. This heuristic measures the change in prediction accuracy if we take a given ...
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1answer
223 views

Choosing one variable from each of 3 buckets of variables

I have a regression model that looks like the following glm.nb(formula = y ~ Gender + Age + x1 + x2 + x3, data = df) In my problem, there are 20 possible choices ...
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1answer
42 views

Do Neural Networks need “compound” features?

Apologies if I haven't got the terminology quite right. I have a question about Neural Networks, and I'm not sure exactly the best way to ask it! Hypothetically, let's say I have a dataset of houses ...
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5answers
3k views

Detecting significant predictors out of many 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 ...
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1answer
34 views

What does the varImp function in the caret package actually compute for a glmnet (elastic net) object

I am fitting an elastic net model with glmnet via the caret package with 189 predictors and a binomial criteria (a,b) ...
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1answer
77 views

Feature selection when bagging trees/random forest

I want to get a better understanding of feature selection and how the number of features affect performance when bagging trees. I am using Matlab's treebagger and I ...
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1answer
10 views

Multi Categorical Features vs multiple Features for categories

Say I am discretizing continuous data based on percentiles. (I realize this is generally frowned upon, but I am doing this for the sake of experiment) I am trying different percentiles, eg breaking ...
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0answers
12 views

How to predict the predict upper bound of a learning algorithm?

I am selecting features for a Logistic regression classifier. I have tested a lot of feature selection algorithms. however, it seems that there exist a fixed upper bound AUC value for a fix feature ...