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

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Including or discarding a variable based on its frequency

I am trying to dummy code a variable sector which have 20 levels. I am trying to combine some of levels. However I got this situation, if I have a say 2000 observations, how do I decide on the minimum ...
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29 views

methods to perform feature selection in unsupervissed learning

What are the available methods/implementation in R/Python to discard/select unimportant/important features in data? My data does not have labels (unsupervised). The data has ~100 features with mixed ...
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18 views

Feature Selection - Mutual Information with response variable that takes three values

I am trying to calculate Mutual Information scores for Feature Selection. I have successfully implemented the Mutual Information to test each feature against the binary response variable. Each ...
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20 views

How many features to overfit the classification?

Recently, I have got some 'strange' comments from the reviewer of my paper. In my paper, I discussed a novel feature extraction method, and then I compared three classification methods for my binary ...
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24 views

Why does SAS Enterprise Miner keep all dummy variables for a coded categorical variable in stepwise logistic regression?

SAS Enterprise Miner nicely creates coded dummy variables for any categorical variables used in a logistic regression model. When it performs a variable selection using stepwise sequential selection ...
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29 views

Using Leave-One-Out Cross Validation with LARS

I have a kind of obscure question about using the Least Angle Regression (LARS) algorithm for variable selection. If I'm understanding it right, my professor formulates LARS as such: $$\mathbb{min}\ ...
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Setting up feature vectors

I am working on a classification project and I want to use SVM's and/or Clustering Algs. What I am having trouble with is deciding how to set up my feature vectors. I have already decided what my ...
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16 views

Is subtracting the mean from PCA necessary when using an SVD result that is feature scaled?

I've applied SVD to the original data matrix and eliminated insignificant columns and rows from U and V^T respectively using the Sigma values. I multiplied together my optimized U, Sigma, and V^T ...
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9 views

Feature selection: all features vs a subset of them

I am doing a binary classification. The dataset has 3000 samples, and each sample has 10 features. But I find that the performance of using all 10 features is almost the same as that of using only the ...
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How to interpret merits in Weka with ChiSquaredAttributeEval and SVMAttributeEval?

I want to interpret the goodness of attributes using feature selection with 10-fold cross validation. With ChiSquared I get something like this (deletet attributes with merrit was 0 in all folds): ...
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category selection with LASSO

Suppose one has two features: color = {R, G, B} and t-shirt size = {S, M, L} and wants to regress these features on the probability of a sale, call it p. So the model is p ~ color + size. Now, the ...
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Feature selection from wavelet transformation in R

I am new to wavelets. Currently, I am developing a prediction model using time series data. I am using the wavelets package in R. I am taking part of the time ...
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55 views

Maximum Entropy Model for classification, what to use as context & feature?

I'm building a Maximum Entropy Model to classify some text, based on paper "A Maximum Entropy Approach to Natural Language Processing" by Berger et.al. It's similar to POS tagging. Below is some ...
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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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1answer
48 views

Feature selection : how to select the Information Gain threshold?

I am trying to use Information Gain to select features when classifying text with a Support Vector Machine. For each word in our training data, we computed its information gain. Then, we should keep ...
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31 views

Feature Normalization/Standardization before or after Feature Selection?

Should the process of feature normalization/standardization be done before or after the feature selection process?
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recursive feature elemination in R with caret

i work with R caret software package to select the most important features from some set of data. My response is a factor of multiple classes (e.g. nominal ...
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6 views

Nonlinear functions of other features as new features in SVM model with RBF kernel

Can some one give me some conceptual insight on the potential advantages of disadvantages of adding features that are (nonlinear) functions of existing features in training an SVM model with an RBF ...
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2answers
79 views

Model Tuning and Model Evaluation in Machine Learning

Despite my readings (on stack 1, 2, or in literature (Cawley, 2010; Japkowicz, 2011)), I don't find a clear procedure for tuning and evaluating a model in a classification task. I want to perform a ...
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Unstable models, repeated crossvalidation, feature selection

I'm still trying to classify few (about 200) samples in a high dimensional feature space (dim=19) into 3 (very unbalanced) classes. I use an implementation of Least Squares SVM with one vs one coding ...
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40 views

Best feature selection method for naive Bayes classification

i want to make classification with naive Bayes. I have got about 100 Features. Numerical ones as well as categorical ones. Since i want only the most relevant ones to be included for the ...
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18 views

TF-IDF for text classification by taking into account the document class

I am looking for a TF-IDF weighting for text classification (not document ranking/retrieval) which takes also into account the document class. For example let's use the typical spam/not spam ...
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27 views

Removing insignificant variables? [duplicate]

Suppose you fit a linear regression model on some data with 10 variables. The F-statistic shows that 3 of them are significant (p < 0.05) , 2 are within trend (0.05 < p < 0.10), and the other ...
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64 views

Is this normal to have big value of Chi Squared?

I am using chi squared for feature selection in text classification. However when I compute it I sometime have very big values. Like 100, 1000 or even 20000. Is this normal ? I wonder because I ...
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70 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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How to apply feature selection based on tf-idf threshold

Let's say we have the following matrix (typical VSM example): ...
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Find variables selected for each subset using caret feature selection

I am doing feature selection using the command 'rfe' in the caret package (http://caret.r-forge.r-project.org/featureselection.html). This command uses a metric to find the optimal amount of variables ...
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Dropping predictor variables, based on variable of importance, effect of Random Forest Accuracy

I am trying to use Random Forest to accurately predict forested land cover classes using Landsat 7, climatic and geographical data. I have 23 predictor variables and 1 response variable. When I drop ...
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Feature / attribute selection for k-means or other clustering

It seems to me that in literature it is assumed that one knows which features / attributes to choose to characterize an item in clustering. If I have a database with items which have many attributes, ...
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Importance of Time Features

if you have a time series and you want to do some predictions, what time feature should you use ? lets say we are trying to predict how many people visit a certain website, we have data for the ...
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116 views

Feature selection with partial permutation

For feature selection, permutation tests are biased in favor of those categorical variables with a large number of levels [White1994]. Besides, it has been proposed [Deng2011] that partial ...
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Regression by multiple dependent variables with constraints & feature selection

I have a data set of 1000 records. Each record has three dependent variables $y_1, y_2, y_3$ and 100 independent variables $x_1,...,x_{100}$, where the dependent variable $y_i$ satisfies: $0\le y_i ...
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281 views

How to select a subset of variables from my original long list in order to perform logistic regression analysis?

My situation: small sample size: 116 binary outcome variable long list of explanatory variables: 44 explanatory variables did not come from the top of my head; their choice was based on the ...
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24 views

why feature scaling or weighting is important in surpervised learning?

I can understand feature scaling or weighting is important in unsurpervised learning case, because we want an good representation of "similarity". But why it is also important in surpervised learning ...
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Feature selection: permutation test Vs deleting a variable

In feature selection for predictive models, it is usually applied a permutation test. In this test, all the values of one variable are randomly permuted and the prediction accuracy is extracted for ...
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Comparing F-values of covariates in R

I'm new here and have a question regarding ANOVA in R. I have an ANOVA table like this from running anova(model) in R, where ...
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Is linear kernel SVM performance between features indicative of RBF kernel SVM performance?

I have feature set 1 and feature set 2. If a linear kernel SVM performs better ("better" meaning greater classification accuracy) when using feature set 1, does this guarantee that a properly tuned ...
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1answer
14 views

Transform a non-monotonic value before decision tree (concrete example)?

Newbie question here. I am building a toy decision tree to differentiate personal names from business, government, or organizational names, like: AAA ENTERPRISES LLC DBA AAA BBB SERVICE SMITH ...
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What method should I use for this optimization / feature selection project

I'm going to describe a problem and I'm not sure how to best solve it. I will describe the situation. When answering please recommend a method and maybe a software library. I'm using Python for my ...
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48 views

Values of the weights in Adaboost

I have implemented a simple Adaboost algorithm, using several weak classifiers, and when checking the values computed by it there are alphas with a negative value. Is that possible, or is there a bug ...
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46 views

Interpreting results of a factor analysis

I performed factor analysis on R using factanal. Following advice I found on this tutorial, I chose the number of factors as being the number of principal components that capture 90% of the ...
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Choosing a score for factor analysis

I want to perform factor analysis to reduce the number of variables in my dataset (the variables are very redundant). One of the parameters I need to supply to the R code is the number of factors to ...
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R package glmnet: Ridge selects variables

I am using the glmnet package in R. When I set the alpha value = 0, I would expect that no variables are selected. When I look at the coefficients some of them are set to zero. What could be the ...
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Clustering communication patterns to detect multiple identities

I have a data set of communication patterns between chatting agents. Each agent can have multiple profiles or identities. I am interested in developing a way to investigate the similarity between ...
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Feature selection based on information gain papers

I want to apply feature selection based on information gain: I have many features many of which are redundant. I am planning on selecting a feature and then iteratively add features that 'add the more ...
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14 views

Are features present in just one sample relevant for SVM learning?

I am building classification and regression SVM (RBF) models where the features for each sample are indicator values(0, 1) for a set of features exhaustively generated from all samples. There are many ...
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52 views

How to prove the significance of features in classification?

I have a binary classification problem. I have extracted 500 features from a set of 5000 samples using my domain knowledge. In other words, I have got hand crafted features. I wish to prove that ...
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56 views

Number of components in PCA

I believe I have a problem understanding PCA: I would like to use this technique to reduce the number of features of my problem. I originally have 10,000 features and 500 samples. However, the use of ...
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165 views

Anomaly detection: what algorithm to use?

Context: I'm developing a system that analyzes clinical data to filter out implausible data that might be typos. What I did so far: To quantify the plausibility, my attempt so far was to normalize ...
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74 views

Variablity in cv.glmnet results

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