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

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3
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1answer
59 views

“…if the data is linearly separable”

I keep hearing this phrase as a precursor to many algorithms, but I am not sure how exactly one goes about finding out if the data is indeed, linearly separable. Of course, if the data has ...
1
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1answer
16 views

How to find the cause of defect in a process

Suppose a product A undergoes a certain process. This product A is produced at a rate of 8000 per month and out of those in 75 cases defects are generated. In the data set, I have rows corresponding ...
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0answers
10 views

Performing interaction with a very significant predictor drives down p val of other predictors. But does it makes sense?

I'm observing a phenomenon that I can't understand. I have a linear regression setting with categorical vars. A couple of these elicit an highly significant coefficient and low p values. When used ...
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2answers
86 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
33 views

Difference of variable selection and importance estimation

Isn't variable importance estimation a necessary prerequisite for variable selection? Is there any use case where you want to select non-important variables for your model? So, why is variable ...
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0answers
35 views

Multple linear regression, adding one predictor with almost perfect fit make others irrelevant

I found something interesting while playing with some data and linear regression. I built a regression with various predictors, more or less correlated with the outcome. Then I added one predictor ...
1
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1answer
29 views

Variable selection and validation dataset

According to Hastie & Tibshirani, we shouldn't use validation datasets to do variable selection; otherwise, we will overestimate the model fit. However, it seems quite often to select variables ...
0
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1answer
19 views

Feature selection for unknown parametric model

Suppose one has about 500 points of 50 dimensional data that one knows a priori is derived from a parametric model (perhaps with some outliers). Does using this knowledge help in feature selection? I ...
2
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2answers
78 views

Do I need to take out any predictors from multiple regression if I put in some principal components as additional predictors?

I have an assignment which involves one area-level dataset made of $366$ scale variables. I have to perform PCA, compare it with rates of an additional response variable $X$, and comment on its face ...
0
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1answer
39 views

How to interpret random forest importance numbers

I ran randomForest in R package using 7 predictors variables (x1 to x7). I repeated the test with 4 dependent variables (y1 to y4). The importance numbers (IncNodePurity) are plotted in following ...
0
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1answer
207 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 ...
0
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1answer
72 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 ...
5
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3answers
156 views

Regression in $p\gg N$ setting (predicting drug efficiency from gene expression with 30k predictors and ~30 samples)

I have a dataset of 29 cell lines and the IC50 values of a test drug. I want to find a relation between the gene expression profiles of each cell line (nearly 31000 genes) and the IC50 values. My ...
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2answers
132 views

How to find important features in this problem

I was just thinking how ML techniques can be applied in the retail industry. Suppose we have data from a retailer who deals with apparel and cloth in this format and for each item there are ...
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2answers
69 views

How to build a predictive model with a billion of sparse features?

I am making a model to learn a dataset which has a big feature number and sparse samples (I am planning to use logistic regression). The feature number can be as big as 1,000,000,000. It is sparse ...
6
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1answer
950 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 ...
1
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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 ...
1
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1answer
111 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 ...
0
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2answers
473 views

What is a good Gini decrease cutoff for feature inclusion based upon random forests?

I am using random forests to try and determine variable importance as part of feature selection for a model I'm working on, and while I can get ranked variable importance by mean decrease in Gini from ...
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2answers
42 views

Feature Selection using (low) MCC

I have approximately 1200 input parameters that I am trying to whittle down with the following rough process: 1) Fit rbf SVM with n = 1200 parameters and calculate Matthews Correlation ...
0
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1answer
35 views

NLP tokenization for building feature vector

I am trying to match new product description with the existing ones. Product description looks like this: Panasonic DMC-FX07EB digital camera silver. These are steps to be performed: Tokenize ...
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0answers
10 views

Development data set for feature engineering and data exploration

I dont hear this being talked about much: If you want to engineer features and visually explore the data, should you do this on a development set separate from the training and test set? If ...
1
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1answer
179 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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2answers
158 views

Evaluating features and similarity measures

I am currently developing a classificator, 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 ...
1
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1answer
208 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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0answers
24 views

Machine Learning: Potential Reasons of Precision Change after New Features are Added

My baseline model uses 10 features $[f_1, f_2, \dotsb, f_{10}]$. Now I have two new features $f_{11}$ and $f_{12}$. New models that use either $[f_1, f_2, \dotsb, f_{10}, f_{11}]$ or $[f_1, f_2, ...
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0answers
15 views

selection of features with Weka

I have a question and I hope that you can help me: I have a bilingual text(source language and target language). I extract from this text the best source phrase and the target phrase related to this ...
2
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1answer
164 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 ...
0
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1answer
37 views

After Clustering, how can I evaluate which features had the biggest impact?

I've just performed unsupervised clustering (using DBSCAN) on a dataset for which I have no expert knowledge on. I'm interested in working out which features had the greatest impact on my clustering. ...
0
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1answer
26 views

Chi-squared Vs Mutual information

Is chi-squared feature selection better than Mutual information based feature selection mechanism?
2
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2answers
79 views

Backward feature selection with CV model selection

I am thinking about doing the following to a data set with $N$ samples and $m$ features 1) Train using semi-supervised learning and cross validate on labeled data using LOO-CV to select the best ...
5
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5answers
8k views

Using LASSO from lars (or glmnet) package in R for variable selection

Sorry if this question comes across a little basic. I am looking to use LASSO variable selection for a multiple linear regression model in R. I have 15 predictors, one of which is categorical(will ...
0
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1answer
26 views

Effect of combining features on classification

I have 2 string features F1 and F2 based on which I am trying to perform classification. I have two choices, either to use the ...
0
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0answers
43 views

Advice for feature selection or feature extraction with semi-supervised learning

I am trying to solve a semi-supervised learning problem using LaplacianSVM. However, before applying LapSVM I would like either to perform feature selection or feature extraction. Furthermore, after ...
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0answers
8 views

Modeling 2-D data as vector

As part of my class project i had to use decision tree classification on a training set which contains a set of matrices where each row is a vector recorded at a particular time stamp and each row ...
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0answers
11 views

Can unsupervised feature learning be used to develop features reflecting patterns in human relationships?

Unsupervised feature learning has been used to learn features for objects and action classification and for emotion detection in speech. My questions are: (1) Is there any existing research ...
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0answers
30 views

regarding using Lasso and Random forest based on the variable selection result coming from other processes

After the process of data exploration process and discussion with client, we set up a set of variables as follows: T1, T2, T3, T6, T8, T2*T3, T1*t6 During ...
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1answer
57 views

How to handle missing data in a small $n$ large $k$ machine learning scenario?

I have a sample size $N=130$ and $1000$ variables. I am using machine learning techniques (SVM) for analysing the data. Some variables in the dataset have values that are so huge that they must be ...
1
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1answer
89 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 ...
1
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1answer
34 views

Stratified sampling for creating test/training sets when there are continous and categorical variables to consider?

Assume a simple clinical study with N=200. Half of the participants are men and half of the participants are women. The hemoglobin of the participants ranges between 80 and 150. There's also several ...
0
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1answer
51 views

Bi-normal separation feature selection (BNS) in R

I'm doing binary classification on highly dimensional text data, with a biased class distribution. After reading this paper, i found out about BNS feature selection. Is there any package that ...
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0answers
15 views

Classification with two different dataset

I am working on a cancer classification model.Task is ,I am initially given a data set of 500 people and 1000 features.These people are given some kind of treatment(say Treatment 1). Some people are ...
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21 views

How features are representation in deep learning

I'm trying to build a deep neural network for mobile phone data. I've been doing different tutorials, but there are some basics concerning how the hidden units represents features that I really would ...
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0answers
5 views

selection variable algorithm based on conditonal mutual information

Is there anyone who understand the selection variable algorithm based on conditonal mutual information proposed by Fleuret 2004? http://sci2s.ugr.es/keel/pdf/specific/articulo/Fleuret_Fast_2004.pdf ...
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2answers
44 views

How to best to use Continuous value features with discreet values for logistic regression based binary classification problem

This is related to Minimisation algorithm for a mix of discreet and continuous parameters? I am trying out logistic regression to solve a binary classification problem. Though I am feature-scaling ...
0
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0answers
8 views

Non-linear auto-regressive model - preselection of relevant columns

Let us consider a dynamic system with nonlinear auto-regressive evolution such as $$ x_{t} = f(x_{t-1},x_{t-2},\dots,x_{t-d})+\epsilon_t $$ where $x_t\in\mathbb{R}^n$ is vector and $\epsilon_t$ is a ...
1
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1answer
57 views

Grid search for SVM parameters; is this is really how it is done?

Suppose I use nested 10-fold cross-validation with SVM. So, the inner-most loop will go around 100 times. Now, suppose I use a gaussian radial basis kernel function, which needs the parameter sigma. ...
6
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2answers
3k views

Significance of categorical predictor in logistic regression

I am having trouble interpreting the z values for categorical variables in logistic regression. In the example below I have a categorical variable with 3 classes and according to the z value, CLASS2 ...
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2answers
74 views

How to do cross-validation when comparing different feature selection methods?

I am using SVM for a prediction task. My sample size is small, only N=140. Suppose I want to compare the prediction accuracy when using two different feature selection methods. Would it be better to: ...