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

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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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7 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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6 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
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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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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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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1answer
84 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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11 views

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
67 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
29 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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2answers
50 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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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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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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8 views

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
41 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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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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17 views

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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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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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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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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1answer
50 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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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
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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20 views

Feature boosting via rescaling in logistic regression and linear SVMs

If I were expressing a problem in terms of binary features, all encoded as {0,1}, could I boost some features by encoding them as {0,2}? Would the effect change based on whether I used either of the ...
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19 views

Statistical test for feature selection

Suppose I have a procedure to select K features out of M. I repeat the procedure N times on, say bootstrapped datasets, and count how many times feature #1 appeared among selected, denote k1, how many ...
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12 views

Feature selection based on cost function

Suppose that we are searching for best features using an optimization algorithm for a classification model (MLP,SNM,Regression,etc...). We should set a cost ...
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18 views

Select the best point pair in the 2D grid [duplicate]

Suppose in the 2D space we have an array of points, and each point has a weighting factor, which is a float value ranging from 0 to 1. Each point also has a coordinate in the 2D grid. The following ...
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62 views

Why isn't the lasso selected variable even not significant?

I performed a lasso selection using lars::lars for a well normally distributed outcome using a pool of 86 predictors. Here is the plot of the output: The ...
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1answer
37 views

Handle large set of features using SVM

I have a biological dataset with 30.000 features (genes) and 1000 data points (cells). Basically I have two major classes of cells: 1 and 0 with a distribution of 90/10. Now I am trying to classify ...
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26 views

How to deal with categorical features.

Recently I am playing in the famous Big-Data website Kaggle. There is a Display Advertising Challenge. In this competition, you are giving a training file which include huge records. the records is ...
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28 views

feature selection for longitudinal data

I have a longitudinal data which looks like this. Number of time points are different for each ID. Y is the binary response variable (take values 0 & 1) and X1-X20 are either continuous or ...
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12 views

Does within-group heterogeneity negatively impact random forest classification?

I have two rather conceptual questions about random forest classifiers. Before we get there, I quickly want to lay out the problem I am working on: I have large a large data set consisting of 300 ...
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36 views

Triple nested cross validation

I have read several very informative posts including the link about the nested/double cross validation, which can determine (sub)optimal hyperparameter values as well as make an unbiased estimate of ...
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21 views

Considering non-i.i.d. covariates in random forests

Random forests are theoretically funded on the assumption that the data are i.i.d. realizations from a multivariate random vector $(X_1, \ldots, X_p, Y)$. Does it make sense to use random forests (for ...
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24 views

How to deal with a feature relating to what type of expert labelled the data that becomes unavailable at point of classification?

Essentially I have a data set, that has a feature vector, and label indicating whether it is spam or non-spam. To get the labels for this data, 2 distinct types of expert were used each using ...
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33 views

categorical feature ranking

I would like to rank categorical features by the order or importance in a classification/regression setting. Input There are two features, which are survey questions: "how is your mood?": four ...
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4answers
104 views

Interpreting conflicting results from Random Forest & Logistic Regression?

I am using SKLearn and Statsmodel in python to build a RF and Logistic Regression, respectively. I have a feature that the RF indicates is important (feature importance of 0.202, closely behind #1 ...
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48 views

Find linear SVM feature weights using libsvm

I'm trying to use linear SVM to do some feature selection. I'm using libsvm, but I cannot figure out how to find feature weights. The model file created looks something like this: ...
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10 views

How can the quality of features be evaluated in high dimensional classification tasks?

I am currently experimenting with on-line symbol recognition for mathematics for my bachelors thesis. I have 369 symbols which I would like to distinguish. There are a lot of preprocessing methods / ...
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21 views

chi-squared feature selection

I have a feature vector of size 250 x 35. That is I have 250 images and each image has 35 features in it. I need to do chi-squared test to get the most significant features out of it. anyone know ...
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1answer
31 views

mixing binary and real-valued features with SGD

I'm going to be using a logistic regression model and using SGD to determine the feature weights. Is it OK for me to use a mix of binary and real features, without doing anything like scaling or ...
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20 views

Feature selection with a binary dependent variable

Given we have a binary dependent variable and 100s of features and ~50k observations, is there a generally accepted way to trim the features via some type of machine learning concept? I was trying a ...
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22 views

On population variable importance

Consider we run a random forest on $n$ independent realizations of a random vector $(X_1,X_2,X_3,Y)$ assuming $Y$ is a numerical response variable. Let $f$ be the best theoretical classifier defined ...
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22 views

Featuring Engineering from Trends in the Training Set

I have a predictive model with just OK performance, and I'm trying to improve it with feature engineering. My question: is it valid to create new features by looking at trends in the training set? ...
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11 views

When to cluster features for supervised learning?

I'm doing a project on dog adoption patterns, and I realized that there are many (100 +) different breeds of dogs. I'd like to build a predictive model using breed as covariate, but I'm not sure ...
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2answers
62 views

What interactions to include in my GLM model?

I realize this might be a too general question, so I'll describe what I'm doing right now first. I'm working for a virtual insurance company and I have this dataset. It has severity (meaning ...
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1answer
55 views

Caret: customizing feature selection, nested inside cross validation

Using caret, I want to train a SVM classifier and estimate its performance using repeated cross validation. My dataset has a very large number of predictors (300K) and I want to reduce this number ...
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6 views

Design a feature with time and presence information

Context: I am working on a decision tree classifier, trying to classify businesses as to whether they are likely to have an event occur (default) in the next 90 days. One input I get is whether, and ...