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

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Are features present in just one sample relevant for SVM learning?

I am building classification and regression SVM (RBF) models where the features for each sample are indicator values(0, 1) for a set of features exhaustively generated from all samples. There are many ...
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55 views

How to prove the significance of features in classification?

I have a binary classification problem. I have extracted 500 features from a set of 5000 samples using my domain knowledge. In other words, I have got hand crafted features. I wish to prove that ...
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1answer
63 views

Number of components in PCA

I believe I have a problem understanding PCA: I would like to use this technique to reduce the number of features of my problem. I originally have 10,000 features and 500 samples. However, the use of ...
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3answers
577 views

Anomaly detection: what algorithm to use?

Context: I'm developing a system that analyzes clinical data to filter out implausible data that might be typos. What I did so far: To quantify the plausibility, my attempt so far was to normalize ...
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1answer
658 views

Variablity in cv.glmnet results

I am using cv.glment to find predictors. The set-up I use is as follows: ...
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1answer
52 views

Feature selection for classifier

I'm using a supervised machine learning algorithm on some big data. There is much more features than observations. To reduce the number of features, I would like to do some feature selection. However, ...
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81 views

Feature extraction for customer churn data

I have a customer churn data, and would be implementing algorithms (decision tree, logistic regression, segment analysis). I have doubt on feature extraction procedure though. The training sample has ...
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1answer
16 views

Identifying feature values that influence an outcome

I have a data set which has data about 1 million people. Data about each person consists of a 'Score' and about 100 features (each of which refers to some characteristic of the person - example - age, ...
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1answer
43 views

Selecting features manually and proving the rest are redundant

I'm working with a gesture dataset, where each gesture has a variable number of frames, and each frame has the 3d position of 20 joints, so that each gesture is represented by a matrix of size frames ...
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22 views

Replacing categorical variables with historic response rate

In Linoff and Berry's "Data Mining Techniques" they mention reducing the number of categorical variables in a classification model by replacing the variable with the historic response rate. "When ...
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42 views

Methods for temporal patterns extraction

For example a video or series of images, or usage patterns data on a website, or a univariate time series, is there some flexible methods for extracting patterns of any length, such as head ...
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1answer
129 views

AICc results in R

I used the model: ...
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1answer
556 views

Gini decrease and Gini impurity of children nodes

I'm working on the Gini feature importance measure for random forest. Therefore, I need to calculate the Gini decrease in node impurity. Here is the way I do so, which leads to a conflict with the ...
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3answers
329 views

Feature Selection

I've a facebook users dataset in which each user has a "huge" set of attribute, i.e about 220 attributes like age, hometown, religion, and a set of facebook liked pages to store the users tastes. Now ...
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8 views

Redundant feature generation

I'm evaluating a few supervised feature selection algorithms, with a focus on redundancy. As a base correlation statistic, I'm using Mutual Information - so discrete variables are also a focus for ...
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12 views

What kinds of features are worth taking from objects on images?

I managed to label and get set of pixels for every single object of interest in the image. Now I'm looking into clustering and am not sure what features are worth using. I currently have two in mind ...
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45 views

Feature selection for one class SVM

I have around 300 features, i need to choose features for one class svm. can some one tell me the ideal algorithm for this use case. I know about that for feature selection regularised random ...
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1answer
167 views

Does Boruta feature selection (in R) take into account the correlation between variables?

I am a bit of a novice in R and feature selection, and have tried the Boruta package to select (diminish) my number of variables (n= 40). I thought that this method also took into account the possible ...
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11 views

How to train input feature weights for a path optimization problem?

I am working on a general path optimization problem. As many of you know, once the weights on all nodes are determined, one may solve this problem in many different means. Unfortunately, my raw input ...
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1answer
27 views

Using selected features from a wrapper algorithm to train another model

I was wondering if it can be useful to use selected features from a wrapper algorithm (for example SVM-RFE) to train another classification model like k-NN or Linear regression.
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2answers
58 views

Feature selection with SNP datasets

I'm working with a Single-nucleotide polymorphism (SNP) dataset with over 2.2 million features and roughly 2000 samples. I wish to do feature selection on this dataset to reduce it down to ...
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29 views

Is mRMR feature selection sensitive to imbalanced dataset?

I wish to perform feature selection on a dataset using mRMR but my classes are imbalanced roughly 3:1. Should I down sample the majority class or use the whole dataset? If I use the whole dataset will ...
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1answer
27 views

Updating set of probabilities for sampling with features importance

I'm currently working on some algorithm and I'm kinda out of idea for a problem I'm trying to tacle. Basically I'm trying to subsample the features of a dataset. I want to subsample that given this ...
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214 views

How to select a model in quasi-poisson GLM with interactions using drop1 command?

I want to evaluate the effect of three factors (one categorical, and the other two continuous) on the response variable, which is a count data. I have performed 7 candidate GLM models with ...
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35 views

Nonlinear problem feature selection

I am trying to make prediction of students course score with neural networks. I have lots of parameters may affect to one course score like sex, GPA and many previous course scores. Before trying to ...
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508 views

HOG Feature Implementation with SVM in MATLAB

I would like to do classification based on HOG Features using SVM. I understand that HOG features is the combination of all the histograms in every cell (i.e. it becomes one aggregate histogram). ...
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1answer
108 views

Identify original features corresponding to high singular/principal component values [duplicate]

In MATLAB, while SVD gives a diagonal matrix S of decreasing singular values, PCA gives a column vector LATENT of decreasing principal component values. How can S be used to obtain a subset of ...
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324 views

HIstogram of oriented gradients (HOG) features descriptor theoretical problems

I'm going to implement HOG as my features descriptor. But there are some things that make me confused: For example: If we have an image with size of 10 x 20 If we want to compute the HOG of that ...
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1answer
165 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
203 views

Grid Search for hyperparameter and feature selection

So I need to select my hyperparameters and also my features. A full grid search of the space of hyperparameters and features is too computationally intensive, so what I am doing instead is for each ...
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51 views

How to determine appropriate number of features and also which features to select?

So I have a dataset which I am using K fold cross validation on to select the number of features and which features should be selected. As I understand it, I would set the number of features to be ...
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45 views

What are some classic examples of feature selection in classification?

Is there a classic example showing the importance of good feature selection in classification? The ideal example would be simple, and very easy to understand. I've been volunteered/instructed to put ...
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1answer
2k views

The main effect will be non-significant if the interaction is significant? [duplicate]

I am using linear mixed models to identify important factors, and it turns out that: A: significant B: not significant ...
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1answer
90 views

What is parameter fine tuning means in SVM?

I got this sentence in one of paper, but I dont understand what does it mean?? "Training a learningbased classifier such as an SVM on an imbalanced dataset often requires parameter fine-tuning, ...
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2answers
38 views

How to determine the factors correlated with observed data?

I have box-office collection data on a number of movies. I also have the production budget, director name, lead actor, actress, language and other meta data related to the movie. I want to know which ...
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1answer
84 views

In decision tree construction, can a good splitter have low information gain?

I have a data set with a candidate splitter variable that is a natural choice from the business perspective. It has two values, and the distributions of the target when conditioned on the two values ...
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3answers
1k views

Can independent variables with low correlation with dependent variable be significant predictors?

I have eight independent variables and one dependent. I have run a correlation matrix, and 5 of them have a low correlation with the DV. I have then run a stepwise multiple regression to see whether ...
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1answer
118 views

SVM basic theory?

I have some questions about SVM: In SVM there is a nonlinear and linear SVM. What is the difference between them? To do classification in SVM, we will find the linearly separable boundary ...
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21 views

Variable Selection Methods in R [duplicate]

regsubsets and stepAIC are the two most common options for variable selection in R; they can be found in the ...
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28 views

Detecting noisy patterns in document images

I am looking for features to extract to distinguish between text objects and arbitrary noisy patterns in degraded document images. This is an example of a document with some parts of noise, I have ...
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12 views

Error trying to reduce my data dimentions [duplicate]

I'm trying to produce a linear regression model, but I only have 25 observations and 34 predictors. I'm trying feature selection, ...
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1answer
210 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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19 views

How to extract “average polyline” from a set of polylines [duplicate]

EDIT: As pointed, there is already a similar question "buried" inside a larger-scope one. I'll reproduce the relevant part here: If I have multiple recordings of the same routes are there any ...
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1answer
81 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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22 views

Segmenting an interval sensibly

Is there a canonical/recommended approach to or algorithm for splitting up an interval with the intent of minimizing the number of segments while keeping a high accuracy? It is essentially an ...
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1answer
186 views

Good algorithms for feature extraction from images?

I am searching for some algorithms for feature extraction from images which I want to classify using machine learning . I have heard only about [scale-invariant feature transform][1] (SIFT), I have ...
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1answer
58 views

Why do Laplacian Eigenmaps ignore the second KKT condition in the solution calculation?

From the original Laplacian Eigenmaps paper, we find the new manifold embedding $Y$ as follows, given the Laplacian and degree matrices $L$ and $D$: $\arg\min_Ytr(Y^TLY)$ subject to ${Y^TDY=I}$. ...
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30 views

Clustering and Feature Selection For Audio Data

I'm very new to stats and I'm at a loss as to where to start with this problem. I'm not sure what tools or methods to use to extract meaningful answers from my data. I'll try and describe the problem ...
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2answers
57 views

Selecting features and estimating their out-of-sample performance with cross-validation

I have only a small dataset. I want to 1. select the most predictive features out of a large candidate pool and 2. get an estimate of their expected predictive performance. In the elements of ...