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

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7
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4answers
775 views

Features for time series classification

I consider the problem of (multiclass) classification based on time series of variable length $T$, that is, to find a function $$f(X_T) = y \in [1..K]\\ \text{for } X_T = (x_1, \dots, x_T)\\ ...
0
votes
1answer
247 views

Feature selection with Caret for data with more than one target

I am trying to do some feature selection, having around 3500 variables for about 200 samples. To each sample is associated two numerical values (the expected outcome). I can't manage to make the caret ...
1
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1answer
107 views

2D Shape Feature extraction

What are popular techniques for feature extraction of shapes? I'm doing image analysis, and I want to classify a smooth object (one with smooth boundaries) from a rough object (has a zig-zag like ...
1
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0answers
47 views

Relationship between threshold, number of features and accuracy/error? (text classification)

Are you aware of any research papers that explain a relationship between the following concepts? threshold (removal of features, whose frequencies are greater than or less than a defined ...
1
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0answers
108 views

Number of features and accuracy/f-measure - Who can explain this results?

I'm performing a binary sentiment classification (positive/negative) based on a Naive Bayes classificator and a SVM. To select top k features I use the MRMR algorithm. The model is trained using a ...
0
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1answer
124 views

Partial correlation

I want to create a regression model to predict state crime rate. There are two variables among 10 ( Vi= # of violent crimes per 100,000 population, Vi2 = # of violent crimes per 10,000 population) ...
0
votes
3answers
288 views

Classifier feature importance

If I train a GNB/LDA/kNN/other classifier I would like to know, in the model built, how important are features to classify or which feature(s) drives the classifier. For example in SVM models the ...
1
vote
1answer
595 views

How does scikit-learn perform $\chi^2$ feature selection on non-categorical features?

I'm experimenting with $\chi^2$ feature selection for some text classification tasks. I understand that $\chi^2$ test checks the dependencies B/T two categorical variables, so if we perform $\chi^2$ ...
2
votes
0answers
197 views

LASSO vs AIC for feature selection with the Cox model

I have some questions about the Lasso. After using the AIC or BIC to select a model, the model is fit with the variables selected in order to get the standard errors of the estimates with CIs, ...
1
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1answer
140 views

'Forward Stagewise' option in LARS algorithm

Can anyone help me understand the forward stagewise part in the LARS algorithm? I was reading the R code and could not figure out what is ...
2
votes
3answers
2k views

How should I interpret the p-values (i.e. t-tests) in regressions, and can I use them for feature selection?

I'm trying to do an OLS regression with several independent variables, and want to better understand how to interpret the p-values from doing the t-tests on the independent variables within my ...
3
votes
4answers
532 views

Decision Tree as variable selection for Logistic Regression

I have to do a Logistic Regression, and have to use a subset of the variables. I received this "tip": do a Decision Tree first, and use the most relevant variables in the Logistic Regression. Is this ...
-1
votes
1answer
150 views

Random forest like procedure for regression or other statistical models

I'm wondering if there exist methods similar to one used in random forest algorithm - I mean taking simultaneously bootstrap sample and random subset of features, then building statistisal model. Have ...
1
vote
2answers
775 views

Feature selection using caret + repeatedcv

I am using caret and repeatedcv with repeats for feature selection. That is, ...
1
vote
0answers
390 views

How to select the best variables by RandomForest in R?

I have a table of mRNA levels of my target gene and it's transcription factors in many different condition. What I want to do is to select the most important ...
1
vote
0answers
190 views

Bootstrap randomized Lasso selection for a Cox model

I'm interested in variable selection for a cox proportional hazards model. I've read this article which slightly favours randomized bootstrap lasso selection over bootstrap lasso selection since it ...
2
votes
1answer
1k views

What is “feature space”?

What is the definition of "feature space"? For example, When reading about SVMs, I read about "mapping to feature space". When reading about CART, I read about "partitioning to feature space". I ...
1
vote
1answer
226 views

How to define in R the most important variables?

I have a data set with my target gene and more than thousand transcriptional factors somehow correlated with this gene. There is data of these factors in more than 70 variable conditions. What I'm ...
2
votes
0answers
170 views

time series with different length: feature extraction and classification [on hold]

I have a binary classification problem, where each data point is multi-channel time-series, which can be represented as a matrix $T \times F$, where $T$ is the time-series length, and $F$ as the ...
3
votes
0answers
134 views

Fast algorithm for variable selection

The (training) data contains 1280 observations with 1415 features. The test set has additional 380 observations. The data is sparse, that is, many of the variables has many zeros and few positive ...
1
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0answers
122 views

Univariate feature ranking in classification

Scikit-learn has function to evaluate the F-statistics for univariate feature importance feature selection. According to the web page they are calculating ANOVA F value. If I understood correctly, ...
2
votes
1answer
137 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 ...
1
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0answers
208 views

Feature importance

The extremely randomized trees classifier (scikitlearn) provides a (multivariate) feature importance measurement Ensemble methods/feature importance evaluation. For each feature, the classifier ...
2
votes
0answers
196 views

Kernel in PenalizedSVM R package

There is not option to select kernel in penalizedSVM R package. What kernel do they use? Is there some other R package with penalized SVM methods where I can choose various kernels?
3
votes
1answer
56 views

Methods for teasing apart the influence of different time series features on a target feature?

Are there any established methods for teasing apart the influence of different time series features on a target feature? To illustrate: The target: Sales volume of kittens. Features: Time of year, ...
1
vote
1answer
81 views

When is the PMI value good or bad?

Pointwise mutual information is calculated by this formula $pmi(x;y) = log(p(x,y)/p(x)p(y))$ , my question now is, When is this pmi good and when is it bad. I know if the value is low it is bad, but ...
2
votes
1answer
170 views

Multiclass classification with SVM a question about the feature vectors

I was told I had to direct my machine learning questions to this site. So here it goes. I'm trying to do Multiclass classification with SVM. I have 7 classes. Now I was wondering if the following is ...
3
votes
1answer
81 views

Changing variable values and examine the outcome difference between the altered and original data

I recently read an approach which is used to find the effect of changing an independent variable. They are doing a classification problem, so each data row (or record) is associated with an outcome ...
-1
votes
1answer
67 views

How logical to select features with respect to the correlation matrix and weigthing?

Is it logical to name low correlated features as valuable and choosing the low corelated ones for classification? Or it depends on the algorithm used for the purpose? How do I need to interpret a ...
1
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0answers
67 views

Overfitting a linear Linear Discriminant Function

I am estimating a Linear Discriminant function with 250 input variables over 4000 data records. Should I consider feature selection, am I over fitting the model? How do I know when feature selection ...
3
votes
2answers
85 views

Random search for the optimal number of input features and optimal number of hidden layers for a MLP?

I've performed a random search in hypothesis space $$\{(c,h)| c \in U[1,256]; h\in U[1,100];c \in \mathrm{Z} \text{ and } h \in \mathrm{Z}\}$$ that defines the parameters of a standard multilayer ...
2
votes
1answer
155 views

Selecting optimal number of input features and optimal number of hidden layers for a MLP?

What is the best way to select parameters for a binary neural network classifier? More specifically I have 265 features ranked according to Mutual Information Criterion. I have to determine the ...
2
votes
2answers
83 views

Can feature selection be considered a way to observe relationship between variables like correlation?

In correlation we can observe relationship between a pair of variables, let me call it X1 and Y. Now, considering I have the predicting variables X1, X2, ..., Xn and the variable Y. Does the ...
1
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1answer
155 views

highly correlated features and high ranking

I am classifying different texts and I wondering about some features that are highly correlated. I have 49 features. Some features are absolute counters (integers) but most features are relative ...
3
votes
1answer
257 views

Why can't Bayesian variable selection be used with categorical variables with more than 2 levels?

I am reading this article which is the first approach on Bayesian variable selection. In the discussion section it says that one of the major limitations of the particular method is that it cannot be ...
4
votes
2answers
202 views

Why does increasing the number of features reduce performance?

I'm trying to gain an intuition as to why increasing the number of features could reduce performance. I'm currently using an LDA classifier which performs better bivariately among certain features ...
3
votes
0answers
159 views

Sensible to include ratio as a variable in logistic regression?

I'm creating a generalised linear regression using a binomial link function for two variables A and B. From looking at the data it appears that A/B may have discriminatory effect. Is it sensible to ...
6
votes
2answers
287 views

Fisher Distance for feature selection

I'm currently working for EEG signal classification from 3 electrodes. I want to have a simple feature selection algorithm that is independent with the classification process. From the feature ...
4
votes
2answers
1k views

Number of trees for Random Forest optimization using recursive feature elimination

How many trees would you suggest to pick to perform recursive feature elimination (RFE) in order to optimize Random Forest classifier (for binary classification problem). My dataset is very ...
0
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0answers
145 views

What are the good algorithms for feature extraction for large dataset?

I have KDD dataset for detecting fraud actions on networks but it has millions of lines and >20 feature columns. Thus it is not viable to process all these on my personal computer. I am thinking about ...
5
votes
5answers
869 views

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, ...
1
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0answers
179 views

Adaboost feature weight calculation

I thought I understood Adaboost, until code analysis made me realize that sample_weight is not an array of the feature weights... and after further investigation I am left confused as to how ...
3
votes
3answers
4k views

How does one interpret SVM feature weights?

I am trying to interpret the variable weights given by fitting a linear SVM. (I'm using scikit-learn): ...
3
votes
2answers
219 views

How to do feature selection for learning from positive and unlabeled examples?

I have a binary classification task for German webpages for which I only have positive examples. That is why I use learning from positive and unlabeled examples as described on this page, also known ...
3
votes
3answers
737 views

How to reduce the number of variables in cluster analysis?

I've got 10 (yes, only 10) cases over 1000 variables (e.g. measurements of concentrations of 1000 different compounds at 10 different time points). I can group these cases into 3 clusters in ...
1
vote
2answers
263 views

Clustering time series with wavelets in R

Can discrete wavelet trasform be used for feature extraction from time series in order to cluster them? Any R code how to do this will be appreciated.
2
votes
0answers
83 views

Random forest like techniques (bagging, random feature subset) for SGD methods

Are there any well-known results/tools/literature on using bagging and random feature subset selection for regression or SGD-based methods?
5
votes
6answers
519 views

What machine learning algorithms are good for estimating which features are more important?

I have data with a minimum number of features that don't change, and a few additional features that can change and have a big impact on the outcome. My data-set looks like this: Features are A, B, C ...
6
votes
1answer
485 views

If p > n, the lasso selects at most n variables

One of the motivations for the elastic net was the following limitation of LASSO: "In the p > n case, the lasso selects at most n variables before it saturates, because of the nature of the convex ...
7
votes
4answers
1k views

Low classification accuracy, what to do next?

So, I'm a newbie in ML field and I try to do some classification. My goal is to predict the outcome of a sport event. I've gathered some historical data and now try to train a classifier. I got around ...