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

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3
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2answers
296 views

Cross-validation and feature selection of a multivariate regression

I've been trying to create a multivariate regression model to fit my training data into the prediction of a value. I've put my data into a matrix X with ...
1
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0answers
36 views

Linear regression for feature selection

Imagine we regress y on x1...x4. Now, we want to find out if ...
3
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2answers
66 views

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 ...
3
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1answer
88 views

Interpreting the lasso coefficients

I have used lasso logistic regression on some data and I have some non zero coefficients for some of the features. I want to know based upon the coefficient values how do I rank the features?
0
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0answers
42 views

Feature selection for support vector regression with time series as features

I would like to select features for a support vector regression for forecasting. I would like to forecast a value at point t with the values t-1,...t-x as features. Now I want to select the most ...
0
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1answer
44 views

How to compare features and classifiers which achieve perfect accuracy?

So I'm looking to compare different combinations of features and classifiers. But I'm getting a lot of combinations that achieve 100% cross validation accuracy. I'm trying to figure out how I would ...
0
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0answers
54 views

Is it possible to determine the set of variables contributing the most to first two principal components? [duplicate]

TL;DR: Is it possible to determine the set of features (covariates) that contribute the most to the first two principal components, and if so, how? Long version: Let's say I have a data table where ...
2
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3answers
37 views

How to compare features and classifiers which achieve perfect accuracy?

So I'm looking to compare different combinations of features and classifiers. But I'm getting a lot of combinations that achieve 100% cross validation accuracy. I'm trying to figure out how I would ...
2
votes
1answer
45 views

Feature selection while retaining a specified feature

Pardon if this question is very basic, but I am not able to find any solution for my problem. I am trying to run a feature selection scheme on N features for my classification model, however I want ...
0
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0answers
26 views

Proper way to determine attribute feature selection's smaller subset based on result metrics

Overview My goal is to predict survival of an instance for five different time periods (binary attribute). I have a 100,000-instance dataset with 40 attributes and I want to reduce the attributes ...
4
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1answer
110 views

Variable selection for regression - the subselect package

No regular here will be unaware of the perils of using stepwise and similar automatic methods for variable selection in regression analysis. But preferred alternatives, such as the lasso or ...
1
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0answers
46 views

Music classification into genres

I need to classify music (songs) into genres (rock, french house, trash metal, etc). My idea was to extract features from the songs (bmp, zero crossing, etc) and then apply known classification ...
0
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0answers
71 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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0answers
57 views

Classifier predicts only one class

I was trying myself in kaggle CIFAR competition, I trained lots of classifiers but get the same result/fail (don't know how to treat them), maybe someone could help me figure what i'm doing wrong. ...
0
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0answers
17 views

How to interpret the selection of a variable in a model (GLM) when spatialautocorrelation is included?

This is a pretty straightforward question. I am comparing outputs of two models (binomial GLM) one including environnmental-only (ENV) variables and one including environmental and spatial variables ...
3
votes
1answer
439 views

Understanding the output of C5.0 classification model using the CARET package

The C5.0 classification model was used in this 4-class problem data with $N_{train}$=165, $P$=11, using caret R-package by ...
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0answers
27 views

Estimating confidence of a prediction

Given a set of features vectors $X=\{\vec{x}_1,..,\vec{x}_n\}$, binary ground truth data $Y=\{y_1,..,y_n\}$ and continuous prediction $\bar{Y} = \{\bar{y}_1,..,\bar{y}_n\}\in [0,1]$, I want to perform ...
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0answers
58 views

Feature selection methods comparison

I recently run a project that involves a feature selection step before further pattern recognition. The number of features for our data set is very large and instead of running greedy ...
1
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2answers
124 views

The curse of dimensionality? (linear SVMs)

How do you know whether you suffer from it? Let's suppose I have a 2 class problem - 2000 training examples and 30 features. While it works good for the most part, sometimes I get edge cases that ...
1
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1answer
38 views

Algorithms/methods to create more features of a limited amount of features?

So, let's suppose that I have a set of 20 features - some of them are continous and some of them are binary. Is there an algorithm/method/solution to create more features ( combine/transform ) those ...
2
votes
1answer
179 views

How to interpret this cross-validated sparse LDA figure using CARET package?

Training data with $p$ =11 predictors and $n$ =165 with 4-class problem was cross-validated (5 times repeated 10-fold CV) using the sparse LDA (aka SDA) using caret ...
1
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1answer
38 views

SVM - combining binary and continous representation of the same feature?

How would this influence the accuracy of the SVM model? Let's suppose that I have one variable which max value is 100 and minimum is 0. Currently, I send it to SVM as a single continuous feature, ...
1
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1answer
26 views

Significance Test for Comparison of Variables

I am not sure how to ask this question without giving an example. I am trying to measure the "cleanliness" of office buildings. I have two variables that try to measure this. Variable one is a ...
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1answer
99 views

Using Principal Components Analysis for feature selection

I have a dataset D made of m samples, and n features with n >> m. For each sample I have a score s which I would like to ...
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2answers
239 views

Mixing continuous and binary data with linear SVM?

So I've been playing around with SVMs and I wonder if this is a good thing to do: I have a set of continuous features (0 to 1) and a set of categorical features that I converted to dummy variables. ...
2
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2answers
136 views

Selecting a feature modeling approach for text classification

I am new to text processing. Currently I am trying to determine which type of feature vector I need for a classification problem. I am mainly deciding between binary feature modeling and ...
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0answers
18 views

Neural net: variable distribution, validation and number of variables - unexpected results

I have been experimenting with ANNs on one of my datasets, they seem to have the potential to be quite effective in explaining my Y variation. Something i am finding is that they very much benefit ...
2
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1answer
103 views

Are there any techniques that quantify the importance/signification of individual attribute values of a particular data point?

Are there any techniques that quantize the importance of individual attribute values in a particular data point, in terms of the attribute's overall importance/signification/contribution to the ...
0
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0answers
66 views

equivalent of PCA explained variance ratio for SVD ?

i am wondering if there is an equivalent of PCA explained variance ratio for SVD. What are the measures I can get to monitor the number of columns I keep after the SVD ? Are any of these metrics ...
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0answers
27 views

Investigating 'minor' effect variables?

Is there any way to investigate minor contributing $X$ variables in a model when there are one or two $X$ variables which contribute to the explanation of a majority of the variation in the $Y$ ...
5
votes
1answer
134 views

Explain steps of LLE (local linear embedding) algorithm?

I understand the basic principle behind the algorithm for LLE consists of three steps. Finding the neighborhood of each data point by some metric such as k-nn. Find weights for each neighbor which ...
0
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0answers
40 views

Feature Selection: markov blanket filter

I need to do a markov blanket filter for feature selection for highly unbalanced datasets. There are popular algorithms to do this? I need to understand the algorithm behind this. From what I ...
0
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0answers
25 views

Choice of 0 or -1 for failure in the independent variables of a logistic regression

I am performing some exploratory analysis on a dataset where the dependent variable is a dichotomous variable. I have ~10 explanatory variables, some of which are dichotomous observations. I am ...
0
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2answers
66 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 ...
2
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1answer
90 views

How to calculate number of features based on image resolution?

Just covered Andrew Ng's Non-linear Hypothesis of Neural Netowrks, and we had a multiple choice question for determining number of features for an image of resolution 100x100 of grescale intensities. ...
2
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2answers
111 views

improve precision in text classification

I am working on binary text classification using sklearn: The length of each sample is not high (~ 200-500 characters) I use TF-IDF to get important words as TfidfVectorizer(sublinear_tf=False, ...
2
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1answer
71 views

Purposeful selection and confounding

I conducted purposeful selection as outlined in Jewell's Statistics for Epidemiology. The log likelihood tests showed covariates, which I considered to be confounding though not significant in the ...
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0answers
48 views

Inferring dimension weight in a mapping from a triangle to a distribution over its vertices

I have a dataset $(y_i, \mathbf{X}_i)$, where $\mathbf{X}_i$ is a $3 \times n$ matrix of reals and $y_i$ takes a value in $\{1, 2, 3\}$. Essentially, $y_i$ represents a "selection" of the row vector ...
0
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3answers
90 views

Classification performance and the feature set selection

I am now working on a classification problem. The generated feature set can be separated into two group. I did a comparison study: use all of the features; use the features of group 1 only; and use ...
2
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1answer
96 views

penalized regression applications in epidemiology

I am seeking advice on penalized regression models for selecting covariates in epidemiological studies. A difficult tasks I face is feature selection while still attempting to account for confounding ...
2
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1answer
52 views

Should I exclude predictor variables if used to create a new one?

I have a dataset that includes race, gender, income, and family size. In addition, a variable for "sliding fee scale" tier is included, which is determined by income and family size. Should income and ...
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0answers
12 views

How to find entropy of vocabulary terms in multilabel document classification problem?

I have 5 million of document s with varying number of labels for each. I intent to find entropy value for selecting discriminative terms to degrade the size of vocab. However, having that multiple ...
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0answers
22 views

ARIMAX feature selection

I am implementing the ARIMAX model and need to implement feature selection. I have >100 features and a lot of data, so I need a method that isn't too computationally expensive. I tried a wrapper, ...
0
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1answer
137 views

Steps followed when Binary logistic regression when both dependent and independent variables are binary

I had set of binary variables. To apply logistic regression, I have checked association between dependent and independent variables and considered only those independent variables in the model which ...
3
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3answers
1k views

Term frequency/inverse document frequency (TF/IDF): weighting

I've got a dataset which represents 1000 documents and all the words that appear in it. So the rows represent the documents and the columns represent the words. So for example, the value in cell ...
0
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0answers
58 views

Random Forests and Feature selection [duplicate]

First, I split my training set into 10 parts. 9 parts of it, I use as LS and the other one as TS. I now want to do feature selection, so I do feature selection on 9 parts. I use Random Forest to do ...
16
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4answers
520 views

When wouldn't I use LASSO for model selection?

Assume that you need to build a linear model to make predictions for new observations, and that there is uncertainty about which subset of variables should be included in the model. You are only ...
1
vote
2answers
135 views

CV on training set with feature selection

I've got a problem with CV on feature selection. I've used a method, but I don't know it's correct... I split my data into 70% training set and 30% test set I work now with my training set. I do on ...
0
votes
1answer
140 views

Feature selection and cross validation

I'm working on a project and I would like to know if the following strategy is good/correct. Sorry if this is a basic/stupid idea (I'm new to this). The input is a dataset with 2.500 features and ...
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0answers
19 views

Comparing 2 different sets of features

So let's say I have two different sets of features A and B. I'm trying to determine which set of features is the best. I'm using leave-one-out cross validation as the final metric as my data set is ...