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

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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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1answer
654 views

Using Adaboost for feature selection?

Is it okay to use Adaboost to do feature selection (selecting a subset of dimensions $S$ from a high-dimensional feature vector $V$)? I divided the samples into four non-overlapping sets: $A$ ...
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2answers
149 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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2answers
53 views

Feature Selection: Information Gain VS Mutual Information

Setting: Multi-class classification problem with discrete nominal features. There are many references mentioning the use of IG(Information Gain) and ...
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0answers
33 views

Selecting a multiple linear regression model with categorical variables

I am trying to analyze the Berkeley Guidance Study to practice multiple regression models, which has 10 continuous variables, 1 categorical variable (with two categories) and the response variable. ...
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8 views

filter feature selection output and cross validation

If I use a filter method for ranking the features like Relief. suppose I have 100 features with 1000 sample and I used cross validation 3-fold . therefore I have 3 ranks for may features . at the end ...
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1answer
30 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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7 views

Bi-normal separation feature selection (BNS) in R

I'm doing binary classification on highly dimensional text data, with a biased class distribution. After reading this paper, i found out about BNS feature selection. Is there any package that ...
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7 views

Combining features extracted from different parts of the same image

It is about car identification in images. I have an 64x64 image divided into 16 equal windows. I compute a HoG features algorithm in each one. And I am using the concatenation vector resulted from ...
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1answer
78 views

Identify original features corresponding to high singular/principal component values [duplicate]

In MATLAB, while SVD gives a diagonal matrix S of decreasing singular values, PCA gives a column vector LATENT of decreasing principal component values. How can S be used to obtain a subset of ...
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1answer
82 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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1answer
29 views

Tool form Hierarchical clustering

I'm trying to perform a hierarchical Clustering Analysis in a dataset of 40 attributes and +70,000 records, which is mostly composed by categorical variables. I've used Matlab and RapidMiner to ...
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1answer
158 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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25 views

Top K variable that represent entire dataset

There are 100 variables in the dataset. Also, i have extracted some additional information about each variable viz Var1 is correlated (Pearson correlation) to Var21,Var25,Var34,Var45,Var55 ; Var2 is ...
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1answer
41 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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21 views

Least-squares fitting with only optimum features, after Lasso - valid?

Using Lasso reduces the coefficients of features of a model, reducing some to zero, and thereby performing feature selection. The number of features depends on the value of $\alpha$ aka $\lambda$. In ...
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2answers
81 views

Choosing the best featureset for prediction

I have this N sets of features F each with $F_i$ number of features. All the feature sets have 20000 examples and we have 20,000 labels for them. Lets say feature set $F_1$ has 10 features and ...
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1answer
856 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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1answer
124 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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1answer
85 views

(Automated) feature selection in multiple regression with categorical variables

I need a general guide on what are the appropriate approaches to automated feature selection in multiple regression with categorical variables. In my case, I have several numeric and categorical ...
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Why normalized feature weights for linear regression are bad feature importance predictors

I am trying to interpret a linear regression model. I assumed using absolute value of feature weight coefficients as indicators of influence of input variable onto output variable. However, it seems ...
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1answer
74 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
112 views

How to find important features in this problem

I was just thinking how ML techniques can be applied in the retail industry. Suppose we have data from a retailer who deals with apparel and cloth in this format and for each item there are ...
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1answer
69 views

Strategy for Analyzing Data

I have been learning about Machine Learning (via Udacity) and Statistics (via Coursera) the past few months and trying to figure out a good way to combine them for a general approach to explaining ...
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1answer
404 views

What is a good Gini decrease cutoff for feature inclusion based upon random forests?

I am using random forests to try and determine variable importance as part of feature selection for a model I'm working on, and while I can get ranked variable importance by mean decrease in Gini from ...
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1answer
103 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
6 views

Select the most confident variable that has two features

Suppose now I have a group of students, and for each student two measurements are given: one is the height of the student and the other is the weight of the student. Then my question is how I can ...
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1answer
205 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
155 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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32 views

Generate mixture model from data with features

I want to build a mixture model from my data, but using features of my data to calculate each component in the model. The data: For each point I have 34 associated features. Each feature is a boolean ...
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3answers
381 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 ...
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3answers
140 views

Methods in R or Python to perform feature selection in unsupervised 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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831 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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Caret: customizing feature selection using matrix-wise operations [migrated]

Short question: is it possible to use matrix-wise operations in caretSBF$score function? Motivation: When working with big matrices in R, operations that work natively matrix-wise [e.g. rowMeans(X) ...
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1answer
29 views

Use fitted value from regression on subset of features as independent variable

I am working with a relatively large data set with 2K columns and many variables can be grouped together (a logistic regression). So I am thinking can I use fitted value from regression on subset of ...
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44 views

Weka java API: Attribute Selection and Cross Validation

Is there a way to perform Attirbute selection(aka feature selection) (regardless of method) only for the training dataset before passing data for Cross Validation ? I currently think that the only ...
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Advice for interpolating a model

I'm new in Stack Exchange, so I hope no to be off topic. I'm also new in bioinformatics and I was asked to perform an analysis. Briefly, I have a dataset of 29 cell lines and the IC50 values of a test ...
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1answer
2k views

How do you select variables in a regression model?

The traditional approach to variable selection is to find variables that contribute the most to predicting a new response. Recently I learned of an alternative to this. In modeling variables that ...
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Feature/Variable selection to accompany mixed models?

I am trying to conduct an exploratory/data mining analysis to discover what socioeconomic factors best predict grade-school performance in children. I have a dataset with about 50000 ...
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1answer
2k views

Information gain as a feature selection for 3-class classification problem

I am facing a sentiment analysis task where I am using Naive Bayes to classify documents as Positive, Negative or Neutral. I have thought of using Information Gain as my filter for feature selection. ...
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22 views

$\chi^2$ test vs F-test in feature selection

In the context of feature selection for classification, does it make sense to use one filter based on $\chi^2$ test and the other one based on F-test? Or they are "interchangeable"?
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33 views

mtry tuning given by caret higher than the number of predictors

According to this discussion, it seems that the train function of the caret package returns a ...
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19 views

Determine which variable or variables is/are the most efficient to predict the outcome

I have a small dataset (n=74) with a +/- 50 variables, not the best data but I have to work with it. The variables are used to select a product. I want to determine which variable or variables is/are ...
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12 views

Unsupervised feature learning from raw text as a previous step for clasification?

I have a corpus of 2500 opinions, is it posible to use scikit´s restricted boltzmann machine implementation to extract a feature vector as a previous step to a classification task?. What aproach do i ...
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1answer
143 views

Recursive feature elimination with only two classes

Recursive feature elimination (RFE) is a feature-selection strategy. It performs in two nested levels of cross-validation. First it tries to divide the training set into N folds. RFE puts one fold ...
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1answer
65 views

Using topic words generated by LDA to represent a document

I want to do document classification by representing each document as a set of features. I know that there are many ways: BOW, TFIDF, ... I want to use Latent Dirichlet Allocation (LDA) to extract ...
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1answer
51 views

Feature Importance in each fold and repeat after repeated cross validation in caret

this is my first post on Cross Validated so I apologize in advance if I'm not yet familiar with any conventions regarding forum posts. Currently, I'm working on a feature selection task using elastic ...
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1answer
154 views

Using principal component analysis (PCA) for feature selection in regression [duplicate]

I have a dataset $D$ made of $m$ samples and $n$ features with $n \gg m$. For each sample I have a score $s$ which I would like to be able to predict. As the number of features is very high (compared ...
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5k views

Using principal component analysis (PCA) for feature selection

I'm new to feature selection and I was wondering how you would use PCA to perform feature selection. Does PCA compute a relative score for each input variable that you can use to filter out ...