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

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Is validation set always necessary?

Lets say I did the following steps: Used some separate development set to select some features. Decided a priori to use only one learning algorithm (SVM) with only default parameter values. Trained ...
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29 views

Feature extraction based on correlations

I have a small problem regarding feature extraction with correlation. I have divided my question in four parts hoping that somebody can help me. I have a dataset consisting of fMRI images. Each image ...
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17 views

Normalization before feature selection such as Fisher score (F-score)

The Fisher score (F-score) for feature selection is defined as in Combining SVMs with Various Feature Selection Strategies. However the paper doesn't mention any preprocessing to feature values such ...
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25 views

How to determine if a categorical variable affects an independent variable?

I am looking at the wine dataset from the R FactoMineRpackage to find out whether the categorical variable soil type affects the ...
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0answers
10 views

Does adding a grouping variable to a model help or hamper?

Imagine I have a dataset of people where I can find the city and country they live in. The data is such that, given the city, there is only one possible country. For example, given Madrid as a city, ...
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2answers
195 views

Evaluating features and similarity measures

I am currently developing a classifier, 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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1answer
179 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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2answers
316 views

Finding interactions using randomForest

I am trying to use randomForest in R to find interaction terms to add to a model. My plan was to fit trees with maxnodes=4 (two ...
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1answer
1k 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
100 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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23 views

Feature selection of non stationary data

I am working with EEG signals which are non-stationary. I have used spectrogram to analyse the data in specific frequecies. I have to select some features from the specific-frequency time signals ...
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13 views

How to evaluate Features for Time Series

I am new to time series and have a few question regarding evaluating and benchmarking my features for a time series model. The question I am trying to answer is whether my social media features ...
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1answer
194 views

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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18 views

When used for feature selection, does the chi-squared test require the features to be nonnegative?

scikit-learn says chi squared test used for feature selection in classification problems and implemented by sklearn.feature_selection.chi2 requires the feature ...
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24 views

Caret feature selection [RFE] yields different features depending on reference level of binary outcome

I'm using RFE from the caret package in R to select variables to be used in a linear discriminant analysis. The outcome is a binary factor, but depending on which level of the factor is used as the ...
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1answer
69 views

combining multiple classifiers common features

Can multiple binary-classifiers be combined to produce a final output if their feature sets have some common elements? How will this influence the accuracy?
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45 views

Rescaling Features for ML

I have data that is collected every month and I want to perform K-means clustering on each month (both on historical data and on future data). However, it isn't clear to me how best to rescale my data ...
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13 views

Interpret F values of selected features

I have a dataset that contains wines and their ratings. An entry contains the name of the wine, the grapes used, the year and the rating: 'Chateau Pape', 'Pinot Gris', '1983', 93.4 I'm interested in ...
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1answer
492 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
14 views

Is F test used for feature selection only for features with numerical and continuous domain?

F statistic/test can be used for feature selection, e.g. from http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_classif.html#sklearn.feature_selection.f_classif ANOVA ...
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28 views

What kind of feature selection can Chi square test be used for?

Here I am asking about what others commonly do to use chi squared test for feature selection wrt outcome in supervised learning. If I understand correctly, do they test the independence between ...
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15 views

Number of candidate inputs that can be handled by different modelling techniques

Am I correct if I say that some modelling techniques can handle better a larger number of candidate inputs? (If we hold the number of observation constant). Let's say I put around 60 different ...
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19 views

Should I lower the number of features in linear regression?

I want to do use a a regression package in R on a data which is composed of around 100K samples each with 100K features. Should I do some pre-processing to lower the number of features, or can I ...
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15 views

For feature selection: VIF (variance inflation factor) and Correlation Feature Selection with kfold cross validation

Someone asked a question about automatic variable selection for modeling: Algorithms for automatic model selection The community raised concerns that using step() and other methods like that suffer ...
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4answers
664 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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2answers
72 views

Model Selection in Statistics

I have been told not to look at significance level, or not to use forward/backward selection using BIC/AIC for model selection. Let's say, I have 100 survey data with 11 variables and I want to see ...
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20 views

Post-hoc analysis of variable selection

I am using support vector machines & 10-fold cross-validation for a binary classification task. For feature selection, I use the t-test. After doing the classification, I'd like to do a post-hoc ...
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1answer
30 views

What are the plots I can do in order to select predictors?

First thing I do with the data is to check the relation of the variable to be predicted with other variables. To do this I produce simple plots using plot function or qplot function. I do matrix plot ...
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1answer
18 views

How to use a varImp function to select features from training set?

Till now I have used a following flow for training a random forest model. ...
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238 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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122 views

Feature selection before neural network classification

I have a training set of 87 samples and 9480 variables. My predictors are continuous and my response variable is binary. I'd like to use the caret package in R to tune a neural network classification ...
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1answer
51 views

Best way to determine contribution of a variable to regression model

What is the best way to determine the degree of contribution a variable is making by its addition to a regression model. Suppose I have following regression model for OutNumeric which is a continuous ...
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21 views

Feature selection in clustering

I am looking for a method for feature selection in Gaussian Mixture Models. I have a dataset with 2000 records and 40 variables. I tried to use the "clustvarsel" package in R, which use the BIC as ...
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11 views

Panel/Longitudinal Data - Seasonality, Variable Selection

I am analyzing a set of panel data by linear regression. I would like to use a fixed effects model, so I am fitting the model below by OLS: $$(y_{it}-\bar y_i)=\beta (X_{it}-\bar X_i) ...
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35 views

Variable importance using cforest in clustering / unsupervised learning application

I have a data set which I'd like to cluster by using random forest. As I have more than 50 variables, I first want to identify the most important features and subsequently cluster the data set based ...
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1answer
24 views

Differences in correlation for individual and aggregated data

I have a sample of 1 million articles form the web with various features. I'm in the progress of selecting features to use in a metric/predictor for article quality. To get some insight into the data ...
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9 views

Finding redundant parts of a data set for training a nonlinear model

I like to train a nonlinear model based on a data set $D_1$,..$D_n$, where $D_i$ is collected by doing experiments $E_i$. It is possible that $D_i$ is redundant given $D_j$ and $D_k$. Since doing an ...
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Logistic Regression with negative signal

I'm trying to build Logistic regression to identify bots. But I found in my dataset that presence of one feature indicate that this is NOT a bot. Unfortunately this feature is not appearing often ...
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4k views

Variable selection procedure for binary classification

What are the variable/feature selection that you prefer for binary classification when there are many more variables/feature than observations in the learning set? The aim here is to discuss what is ...
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20 views

R feature selection [duplicate]

I am working with the method randomForest for model building. And for a good model performance, it is very important to select the right features. At my example I have 30 variables and I would like to ...
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Looking for a principled/systematic procedure for discarding features

I have a collection of $M_i \times N$ matrices $X_i$ whose rows are (raw) feature vectors (from a common $N$-dimensional feature space). MATLAB reports that most of the covariance matrices $C_i := ...
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1answer
86 views

How to increase the performance of random forest classifier?

I have a text classification task. These are the metrics for different languages at present: class1: 0.6823 class2: 0.7450 class3: 0.66 class4: 0.6719 How can I ...
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1answer
111 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
77 views

Model Selection and RFE using caret

I'm faced with a high dimensional (samples=148, features=20000), supervised binary classification problem. Which I would like to approach with an ensemble of classifiers, that will classify using a ...
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1answer
40 views

How to compare the relative importance of features in GP regression?

Kernel function with different length scales, such as the squared exponential function, is said to be able to quantify the relative importance among the input (predictor) features. The idea is to ...
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15 views

Evaluation of features, how to find which feature is the most effective?

My question is following, which approach should i use in order to make a evaluation of features. To be more specific, for example we have a tweet message: "The weather is nice outside, it makes me ...
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22 views

Including Feature Selection in Cross Validation - Application to Bag of Words

I am working on a prediction problem where I was given a 6,000 record dataset with the value of the dependent variable included ("train"), and a 2,000 record dataset with the same independent ...
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1answer
415 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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0answers
11 views

Feature Extraction for landscape images

I work on a landscape-image database (forests, beaches, landmarks, cities etc.) and I'm trying to find the similarity between them, I don't have labels or any specific classes, I just want to find ...
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2answers
137 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 ...