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

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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29 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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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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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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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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34 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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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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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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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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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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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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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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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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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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Which one is correct phase for neural network or support vector machine? Features or Inputs?

Which one is correct phase for Neural Network or Support Vector Machine? Features or Inputs? Based on ...
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1answer
115 views

Features that correspond to rare events: how rare is “too rare” to be informative?

I am working with 82 binary features constructed from six categorical features. I have about 1,600 observations. Some of these features correspond to extremely rare categories. Some of them have only ...
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24 views

Tree analysis - CHAID cart

I am new to CHAID and want to know how to decide which independent variables I should select to run CHAID Analyis? Is there a technique to select and then apply them and run the analysis? Please guide ...
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Can adding an additional feature to a perceptron classifier make the results worse?

I am using perceptron to solve a classification problem. I have a limited amount of features (26) and iterate through all possible combinations of them. A combination of two features [feature_a, ...
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What will be the simple interpretation for the coefficients for features obtained in any Machine learning models?

I am working with a data that consists of two classes. I have used scikit learn, to craete models using SVM, Randomforest etc.I used to r2_score and I sorted the scores for features I am having and I ...
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How to make use of less data of a particular class for better modeling?

I have a dataset, say 9000 rows, with some features. Around 8000 belong to class 1 and 1000 to class 0. So, if I am creating a model with any method say SVM, LR, Random forest the model has a tendency ...
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Feature Representation for Samples with Different Number of Properties

I want to build a machine-learning classification model that learn from properties (features) extracted from proteins. I represent each sample (protein) using some features (e.g. 100 features ...
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Scale-invariant feature transform explanation

How do I explain the scale-invariant feature transform (SIFT) to a layman?
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I observed very different feature scoring from two different classifiers. What does it really mean?

Here what I've done. Given the dataset, I run a Random Forests and Logistic Regression with 5 Fold Stratified Data Sampling. Then I plot the feature importance for Random Forests and Logistic ...
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1answer
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Automatically fixing ill-conditioning or collinearity

I'm backtesting a regression model, which entails running it on a bunch of bootstrap samples of a "rewound" version of our data set. Unfortunately, in some of these resamplings, I end up getting some ...
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Appending additional data to learnt autoencoder features

My task is to perform image segmentation / full scene image parsing. I am working on an outdoor dataset which was taken under strict spatial constraints. The images contain fruit on trees and the ...
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No significant tests when using Benjamini-Yekutieli multiple testing correction on millions of tests

I am using a univariate filter to reduce the number of features prior to applying a learning algorithm to a huge binary classification dataset (22510066 features x 500 examples). All the features are ...
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59 views

Deep learning: representation learning or classification?

For classification, I have often heard about deep learning / deep neural networks as a form of representation learning. I am confused as to what "representation learning" means in this context. Which ...
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Reference for this claim: important features in data can be “hidden” in the higher PCA axes that are typically thrown out [duplicate]

I remember reading a paper a while ago that demonstrated some cases in which PCA would fail to capture important features of a data set in the first few principal components, but where those features ...
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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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How can be assesed that a given data representation is better than the other?

Given a classification dataset, suppose I learn many different data representation with Matrix Factorization, Clustering or with such approaches. At the end , how would I decide which is better than ...
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Data-driven, high-dimensional feature selection strategies

I am working on a biomedical/healthcare data science problem. I have a dataset of 600 samples, ~6000 variables and class label as "positive" or "negative". I want to perform feature selection on ...
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Classifier with variable number of features

I am trying to make a classifier when each sample has a variable number of features. An example of how this could occur is, for example, if the features are the purchases (type, dollar amount, etc) ...
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How To Better Represent A Problem To A Machine Learning Algorithm

I am familiar with the basics of how to present a problem to a machine learning algorithm using binary encodings. I am also familiar with, but still learning about, feature selection/extraction and ...