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

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How to select important features with multiple datasets?

There are several feature selection methods for dataset, but it is difficult for me to find methods for multiple datasets. The example of my datasets is like below. Sensors are features, and they are ...
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Dimensionality reduction for multivariate time series

I have a data set including 25 variables $(x_{1,t},\dotsc,x_{25,t})$ at each time $t$ and all of this group is repeated through time. I want to explore the relationship between these variables through ...
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10 views

Correlation based feature selection(CFS) tool

Is there any tool or script that was implemented for correlation based feature selection? My feature vector data is in a large-scaled data file, so if I use tools like Weka for feature selection, I ...
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Why do loadings of princomp in R report identical proportion of variance for all principal components? [duplicate]

I'm trying to run a few tests using princomp in R. In princomp there is a value called <...
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1answer
22 views

Consequences of overlap between training, validation and test data

If I'm splitting my data in training, validation and test data to assess different (sub)sets of features for my task. What are the consequences if I (by mistake) split my data incorrectly? In the ...
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5 views

how to define the Features,labels and function in sequentialfs matlab [on hold]

I have the features extracted using LBP for 5 classes of size 645 X 40960 and labels of size 1X645. Now i want to select the relevant feature using sequentialfs..by going through matlab help..i tried ...
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113 views

Can PCA allow to identify redundant variables that can be removed before doing cluster analysis?

I hope this is suitable for this forum: I am new to PCA and what I ultimately want to do is perform cluster analysis on my dataset. I have 20 physical descriptor variables for organisms, each with ...
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32 views

How the Correlation Matrix is built for PCA in Weka?

Just to give a context, I want to use PCA (Principal Component Analysis) to identify which attributes are similar to others, so I can use just one (or a subset) of them. The correlation matrix of n ...
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best debug procedure for caret variable selection 'functions'? [migrated]

I'm trying to use caret's 'filter' functions to do variable selections before training a model using the following code: ...
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1answer
58 views

Feature/variable selection with categorical variables [closed]

My goal is to compare several machine learning algorithms for sales prediction (logistic regression, neural network, random forest, svm -> classification problem, whether the sales will go up or down) ...
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19 views

Correlation for feature selection in multi-dimensional time series

I have multi-dimensional time series data with seven dimensions. The correlation coefficient between two of these dimensions is about 0.65. Can those two variables be said to be/treated like being ...
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12 views

How to deal with missing data when calculating Information Gain

While working on a neural network for classification problem I'm dealing with huge number of possible features and information gain seems like a good way to narrow them down (there are hundreds of ...
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1answer
27 views

Feature importance in gradient boosted trees

I am tuning the parameters of a gradient boosting regression tree algorithm and find it hard to understand the importance of some variables. Here is the case.. when the number of estimators is ...
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1answer
56 views

Challenges in interpretation of variable selection from LASSO and OLS [duplicate]

I work as a consultant and I am often faced with variable selection and prediction problems. For my clients, I run OLS and I am recently pushing for penalized methods which can handle variable ...
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14 views

Can LARS or Coordinate Descent select features that are marginally uncorrelated with the response?

I could construct a response Y the following way: Given $\left\lbrace X_k \right\rbrace_{k=1}^p$, and the regression model $$ Y = \sum_{i = 1}^p X_i - \rho p \beta_{p+1}X_{p+1} + \varepsilon,$$ if ...
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20 views

Testing for feature importance with missing values

I'm looking for an appropriate model to do the following analysis: I'd like to test which courses are the most important in determining if a student stays in or leaves a university program. Imagine I ...
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Covariance-residual technique for linear regression feature selection

When doing forward feature selection for linear regression, it is a well known trick that to select the next feature to add, we can compute the covariance of each candidate feature against the current ...
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26 views

Selecting Subsets with Linear Correlation

I'm looking for a method of grouping 200+ samples with 30+ features into groups which share linear correlations among a subset of the features. I've found Ransac to sometimes return a good ...
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1answer
44 views

Can I use output of classifier A as feature for classifier B?

This is likely to be a confused question, but I'm curious if this is a valid way to combine classifiers. I have a classification data set, i.e. column of labels and N columns of features, and I use a ...
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41 views

Recursive feature elimination and class imbalance

I am trying to apply the recursive feature elimination in the R package caret following the example in the caret website: ...
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39 views

What are some of the best approaches for variable selection in Poisson regression?

My target variable follows a Poisson distribution. I have to make a selection of best variables out of about 2000 variables. Is there any method exist for variable selection for poisson type ...
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1answer
27 views

FInding relevant features for a time series segmentation

I have a time series data, where each of the data point belongs to one of the known clusters. What I am interested is to perform a HMM so that we can obtain hidden states that further abstracts out ...
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3answers
400 views

What are the advantages of stepwise regression?

I am experimenting with stepwise regression for the sake of diversity in my approach to the problem. So, I have 2 questions: What are the advantages of stepwise regression? What are its specific ...
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25 views

Comparing and ranking differentiating attributes across groups

I'm looking for some help on how to approach this problem. Say I have two or more groups of people. Each group has characteristics and attributes. For example, say we have the following two groups: ...
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73 views

Using non-significant variables in model

I am trying to build a credit scoring model and have discovered and interesting approach for feature selection. I am looping through all features and removing them one by one (using variable ...
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1answer
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In feature selection, are there any rules on choosing metrics to mesure relevance? (MI / Fisher score / correlation coefficient, etc)

This is a rather general question. If the question is vague and hard to answer in a few lines, I'd be happy if someone just point me to some readings. Thanks in Advance. I am working on a multi-class ...
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70 views

Does PCA do something else apart from selecting features with the most variance?

While experimenting with Spark library MlLib, I questioned myself if I understood well the mechanism of PCA algorithm, because output of MlLib algorithm was not what I expected to get. so for given ...
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Extension to SAFE screening rule for Lasso

In El Ghaoui et al. (2010), "Safe feature elimination in sparse learning" and following works, screening rules are derived for Lasso (as well as other L1-penalized problems): $ \min_w \|y-X w\|^2 + \...
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29 views

choosing a model after feature selection process

so ive been selecting features for a regression problem and have obtained a list of the best performing feature sets. (note my list is actually several thousand lines long) 188.493 186.989 [379.45, 0....
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25 views

number of features in feature selection for text mining problems

Let's say for a text mining problem (e.g creating a predictive model using text analysis), using a feature selection method (e.g TF-IDF) we come up with 1000 features/words/tokens. Is there some ...
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9 views

Detecting automobile honking [migrated]

I'm detecting vehicle honking from environmental sounds (engine noise, speech music, siren etc.) based on binary classification. Common audio features (spectral flux, centroid, mfcc, harmonicity etc.) ...
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Pedagogical example of feature selection for model building

I am looking for a good pedagogical example use of feature selection for model building. The purpose is to expose students to some very basic methods for feature selection, in the context of boolean ...
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1answer
26 views

Study design using multinomial vs logistic regression?

Suppose that I have a categorical response variable that consists of group 1-3, and I hope to see if predictors can differentiate group 1 vs group 3 (group 2 not included). The response variable is ...
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39 views

How can I find the field which most affects or contributes to decision making in a machine learning algorithm?

Consider the example below. On a larger dataset, it would be fairly obvious that name and gender are not a good indicator of whether a person is an adult or a kid, and that it's age which best decides ...
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Variable selection for predictive modeling really needed in 2016?

This question has been asked on CV some yrs ago, it seems worth a repost in light of 1) order of magnitude better computing technology (e.g. parallel computing, HPC etc) and 2) newer techniques, e.g. [...
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Identifying sequential patterns and deciding which ones are useful

So, basically I have a problem in which I have, over time, the appearance of different features, each feature containing different categories (where categories belonging to the same feature cannot ...
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log loss and squared loss in shrinkage tuning in R?

My model is logistic regression. Is there a way to tune the parameter lambda of lasso or ridge based on cross-validated log-loss and brier(eg. proper scores?) in any R packages? I'm using glmnet ...
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When to use Group lasso over lasso?

Two cases: When should numerous numerical predictors be grouped? is it just based off some theoretical knowledge on the predictors? When should levels(>2) in a factor be grouped together?
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Feature selection for an ordered logit model (R)

I'm using an ordered logit model to predict credit ratings/risk (1-8, ordinal) as a function of 126 predictor variables. (See: https://www.kaggle.com/c/prudential-life-insurance-assessment/data for ...
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Can different data mining algorithms cross check each other's feature selection?

I have worked with the same data set for a little while, using a number of different data mining algorithms. As a result, I have developed a short list of predators which are virtually always useful - ...
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Cross validated loglikelihood?

This is probably a silly question: I was playing around with penalized package and cvl outputs a cross validated loglikelihood and another measure just called loglikelihood which is suppose to be "...
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57 views

Results from rfe function (caret) to compute average metrics - R

I am computing a SVM-RFE model with the rfe function of the caret package, but I am a bit confused about the results. My code is:...
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81 views

More features than data points in linear regression

In a dataset with more features (e.g. 120) than data points (e.g. 60) what are the techniques commonly used to select the best features to apply linear regression? Obviously there is an efficiency ...
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Why use group lasso instead of lasso?

I have read the that the group lasso is used for variable selection and sparsity in a group of variables. I want to know the intuition behind this claim. Why is group lasso preferred to lasso? Why ...
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Why should I choose features or plot them manually when there are built-in functions to do that?

Why should I select variables due to my intuition if there are builtin functions in sklearn python like SelectKBest() and PCA() If I plot graphs of features of the data to see if they can detect the ...
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37 views

General-to-specific subset selection (“Autometrics”) performing well in macroeconomics

I wonder why general-to-specific (GETS) subset selection and particularly the Autometrics algorithm are performing well in macroeconomic modelling/forecasting. How does Autometrics work? Doornik "...
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Is it possible to make the non-separable data more separable by any methods of feature selection, extraction or transformation?

Could these data (in the figure below) be separated by any means of feature extraction, transformation, or it's just a waste of time to make the three classes separable if they "in fact" weren't ...