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

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2
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3answers
470 views

How to divide feature set for selection and training

I have training data with 260 observations that have a total of 7 classes. Each observation has 120 features. I applied feature selection based on the Bhattacharyya Algorithm and got the top 40 ...
1
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1answer
59 views

finding an optimal subgroup of binary indicators

My dependent variable is continuous variable that measures the (potential) success of a person in some activity. I have hundreds of binary indicators, each indicates about the existence of a specific ...
1
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0answers
163 views

Cross validation for variable selection and coefficient shrinkage?

Is cross validation an appropriate technique for variable selection and regression coefficient shrinkage? A former colleague of mine used 10-fold CV to compare the regression coefficients from the ...
0
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0answers
199 views

Metric warning using caret's rfe

I am using the caret package to do feature selection with rfe while training a knn ...
0
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0answers
42 views

Variable selection (automated)

I was wondering whether the following mechanical selection procedure will result in a possible bias. First let me introduce the first procedure, we start with a model and only look at the t-value and ...
8
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4answers
186 views

Lasso-ing the order of a lag?

Suppose I have longitudinal data of the form $\mathbf Y = (Y_1, \ldots, Y_J) \sim \mathcal N(\mu, \Sigma)$ (I have multiple observations, this is just the form of a single one). I'm interested in ...
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0answers
13 views

Sparse variable selection algorithms that account for parameter changes

I am variable selecting for a time-series forecasting model that has parameters sampled from a high variance sampling distribution centred near zero and that undergo changes over time. Each predictor ...
13
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2answers
648 views

Why does the Lasso provide Variable Selection?

I've been reading Elements of Statistical Learning, and I would like to know why the Lasso provides variable selection and ridge regression doesn't. Both methods minimize the residual sum of squares ...
3
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0answers
167 views

When would I choose Lasso over Elastic Net

What are the scenarios where Lasso is likely to perform better than Elastic Net (out of sample prediction)?
2
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3answers
106 views

What if Lasso selects transformed terms but not untransformed terms

Suppose I have standard normal features $X_i \in \{X_i : i \in \{1,...,1000\}\}$. I extend this set of predictors with transformations as follows: $\{X_i,X_i^2,X_iI(X_i > 0) : i \in ...
0
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1answer
151 views

Best subset selection

My statistical learning text claims that for best subset selection, 2^p total models must be fit through regression if for p covariates, we fit p choose k models at each k, k = 1,...,p. I interpret ...
0
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0answers
30 views

Recursive Feature Elimination Fails to Output as Expected

Currently I am using rfe function in the "caret" package to do feature selection. There are 380 variables as input candidates. I have done many trials and I noticed that something weird always ...
0
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0answers
138 views

Find weight of features for feature selection

I have a data set of videos from which I need to recognize the emotion of the speaker. For that reason I have some markers on the face of the speaker. I detect their movement as the speaker speaks and ...
2
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2answers
195 views

Variable selection with groups of predictors that are highly correlated

What variable selection approach should I consider if I have thousands of predictors with clusters that are extremely correlated? For example I might have a predictor set $X:= ...
1
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1answer
102 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 ...
1
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1answer
331 views

Decision trees variable (feature) scaling and variable (feature) normalization (tuning) required in which implementations?

In many machine learning algorithms, feature scaling (aka variable scaling, normalization) is a common prepocessing step Wikipedia - Feature Scaling -- this question was close Question#41704 - How and ...
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0answers
33 views

Confusion related to feature engineering

I was reading this tutorial where they mentioned ...
0
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4answers
135 views

Machine learning input relationships

After learning about a few machine-learning models (NN, SVM, decision trees), I was wondering if these models are able to find inherent relationships when learning. For example, if I feed it two ...
0
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1answer
132 views

How Can I use some variables selected by LASSO?

I am very new about statistics. So, please understand if my question is somewhat awkward, and please give me related any advice. I have some data set. X = 500 x 100 (500 observations x 100 ...
2
votes
2answers
232 views

Variable selection in time-series forecasting

I have a time-series forecasting task and would like some input on variable selection and regularisation. My problem has the following characteristics: 2,000,000 sample size. Most of the time, no ...
5
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3answers
922 views

Selecting the best subset of variables for parsimonious binary logistic regression models

In addition to PROC VARCLUS, randomForest, glmnet, and assessing multicollinearity among potential predictor variables (without regards to the outcome of interest), I am seeking other methods of ...
0
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0answers
46 views

Network/structure learning

Given a data set $\mathbf{X}\in\mathbb{R}^{n\times p}$, where $n$ is the number of samples (observations) and $p$ is the number of features, I would like to know what kind of methods exist for ...
2
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1answer
68 views

Distinguishing two datasets

I have two datasets from some Web store (like Amazon). Datasets have one and the same structure. Each record in these datasets has the following attributes: ...
1
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1answer
36 views

Interpretation of feature selection task

So I am given the following question Data set sample5.txt has a 20-dimensional input $x$ in $\mathbb{R}^{20}$ but we suspect that many of these are actually irrelevant. Could you model the ...
1
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1answer
93 views

k-fold feature selection

I have a data set with 20 K variables. I have tried to select some features via Boruta and FSelector but I could not achieve ...
3
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0answers
93 views

Variable reduction by means of ANOVA?

I have a typical problem with several variables and a large amount of data which are not important right now. The goal of the study is to relate variable $Y$ with variables $X_1,X_2,...,X_n$. I have ...
3
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0answers
82 views

Mutual information/pointwise mutual information for measuring prediction

I want to measure how well I predict a vector $Y$ (vector not a label) for observation $X$. Both $X$ and $Y$ have the same set of features ($1\times n$). For that, I thought of "scoring" the ...
2
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0answers
72 views

Confusion related to feature selection

Well my objective is to predict solar energy radiation at a particular location given some features like wind, temperature, humidity ... I have a total data for 10 years where I have the measurement ...
-1
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1answer
193 views

Implement Forward, Backward, Step and LASSO in VB .NET

My client wants me to implement Variable selection methods i.e. Forward, Backward, Step and LASSO in VB .Net platform including p-value and AIC. I have no idea about the steps involved to calculate ...
1
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1answer
48 views

How to deal with features only available for a few instances?

I am working with a regression problem. I have some features which are only available for a few instances. But with those few instances based on those features we can build a model which gives ...
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2answers
915 views

Random Forest: IncNodePurity and Feature Selection for Binary Logistic Regression

After creating a Random Forest object using randomForest with around 500 candidate variables, I used importance(object) to ...
2
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1answer
199 views

Combining Exploratory Factor Analysis with Random Forest for Binary Logistic Regression Feature Selection

For those of you familiar with Exploratory Factor Analysis (EFA) and Random Forest (RF), I have recently had an idea of combining these two methods to reduce the number of potential predictor ...
7
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2answers
3k views

Feature selection with Random Forests

I have a dataset with mostly financial variables (120 features, 4k examples) which are mostly highly correlated and very noisy (technical indicators, for example) so I would like to select about max ...
0
votes
1answer
299 views

Exploratory Factor Analysis for Binary Logistic Regression Variable Selection

I have a great interest in learning new methods(at least to me) of variable selection in regards to binary logistic regression when I am working with over 500 potential predictor variables and have ...
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0answers
77 views

Feature selection using correlation

I am trying to do some feature selection using correlation. However, I found that my features are not that correlated. The highest correlation was 0.08. So I am not sure if this is a useful thing to ...
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0answers
144 views

Features selection by filter methods for multivariate time series

I have a data set in which the samples are multivariate (about 30 variable/features) time series. These samples refer to two classes. I would like to select the variables more relevant to discriminate ...
3
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1answer
277 views

Using Mutual Information for Binary Logistic Regression Variable Selection

In addition to proc varclus, randomForest, and assessing multicollinearity among potential predictor variables, I am seeking ...
1
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1answer
71 views

Can I select a subset of predictor variables without using step()?

I have a set of 20 predictor variables, and I want to formulate a regression model by applying my own variable selection technique basically with backward approach (just for an experiment purpose.) ...
2
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0answers
66 views

Out-of-bag estimate biased by correlated features

I have a data set with a small number of samples (322) and a large number of features (318.976). My data consists of images, and I want to train a binary classifier. Since I have such a small amount ...
2
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0answers
31 views

Correct order of performing imputation and variable selection

This is a general question about performing data analysis. I have a data set with ~1000 sample size and 200 features. Some of features have more than 50% missing or even higher. The missing pattern is ...
5
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1answer
160 views

A Bayesian perspective on omitted-variable bias (and other covariate-selection bias problems)

As I know OVB, from a frequentist education, when you leave a variable $(z)$ out of your control set $(X)$ that is correlated with both your independent variable of interest (treatment $T$) and your ...
2
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0answers
260 views

Feature importance scores of SVM multiclass one-vs-one design

Info about dataset: 5 classes, 200 trials, 100 features. (I know about the trial to feature ratio being very low, but can not avoid this here and still got well enough classification results.) ...
4
votes
3answers
369 views

How to handle high dimensional feature vector in probability graph model?

I was doing some NLP related stuff which involves training a hidden Markov model, and use the model to segment sentences. For every sentence, I translate the tokens into feature vectors. The features ...
3
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0answers
200 views

Microarray data: suggestions on Feature selection + Model training scheme?

I have a microarray expression dataset (46 samples, thousands of attributes) and I want to perform feature selection first, and, based on this subset of features (shouldn't be more than 4 or 5, based ...
4
votes
1answer
60 views

Which data for feature selection to get unbiased result?

I have a 70 / 30 ratio for train / test data. I have a relatively small feature set (6 features), however, I still want to do feature selection to get rid of any redundant features (I'm guessing 1 of ...
2
votes
1answer
121 views

Number of samples vs Number of features

I've got a set of two classes with 4000 observations total. I've a set of 63 features to construct a predictor. My question is, is there a relation that would prevent overfiting for having too much ...
3
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2answers
244 views

Should feature selection be performed only on training data (or all data)?

Should be feature selection performed only on training data (or all data)? I went through some discussions and papers such as Guyon (2003) and Singhi and Liu (2006), but still not sure about right ...
3
votes
1answer
179 views

Feature selection in the training set

I have a classifier, and I am using leave one out cross-validation to assess its performance. On each iteration, I divide the dataset into training and testing sets. The testing set is just the ...
3
votes
1answer
107 views

Select best distance for feature selection

Suppose I have matrix $X \in R^{n \times m}$, where $n$ is the number of individuals and $m$ is the number of features and $X[i,j] \in \{0,1\}$; $1$ indicates that the individual $i$ has the feature ...
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0answers
166 views

Event Prediction through Machine Learning

I have a large data set consisting of ca. 40 categorical data items and a few interval data items (real numbers, less than 5 such items). Most categories should have a lot of values that repeat ...