Tagged Questions

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

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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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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 ...
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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
12 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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1answer
39 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 ...
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1answer
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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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11 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
3 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
42 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 ...
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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 ...
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0answers
28 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 ...
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1answer
40 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. ...
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11 views

Generating Labeled Training data from 2 data sources for Predictive Classifier

I am trying to build a predictive risk model classifier for an product (classifying good or bad). I am in the process of creating a training dataset. Here are the challenges I am facing. I have 2 ...
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0answers
12 views

Different variable importance results with stabsel and mboost

I'm using glmboost in the mboost package to fit a boosted regression using linear models as the base learner. There are 13200 observations and about 75 variables, ...
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0answers
12 views

Sparse PLS: algorithm for variable selection and model fitting

In the spls package in R (based on the manuscript by Chun and Keleş [1]), there is a separate specification for the variable selection and fitting in the main function, ...
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0answers
4 views

Low memory unsupervised feature selection

I have been working on a project that requires me to cluster data. I am currently using K-Means (The reason for the choice is a very long story, but basically I am stuck with it). So far I have ...
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0answers
9 views

Alghorithm for choosing the best set of words for twitter filtering

I'm using the twitter API to get a stream of tweets. You can't get all the tweets from the public API, it requires you to add some word filters. But you can add up to 400 words for filtering and if a ...
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2answers
61 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: ...
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1answer
47 views

Relationship between Gini Importance and Prediction Performance (say AUC)?

I want to use the decrease in Gini impurity to rank features for my random forest classifier. I understand that the decrease in Gini impurity at one node is calculated as: $$ \Delta i(n) = i(n) - ...
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1answer
45 views

How to analyze elastic net fitted model coefficients

SOLVED: an elastic net model, as any other logistic regression model, will not generate more coefficients than input variables. Check Zach's answer to understand how from an (apparent) low number of ...
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1answer
28 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 ...
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2answers
42 views

Can we learn 3d features using Autoencoder?

Typically, we use Autoencoder to learn 2d features on 2d images (e.g. pen-strokes of digit). For example, if I have 10000 3d 31x31x31 images (e.g. car images). I unroll each of the images, i.e. ...
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Queries in GBM Model in R

I have the following questions pertaining to GBM Model in R. Question 1 : I have a mixture of factorial and numerical data. And Its a binary classification ...
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Regression to chose questions which better correlate with a 10 points likert like score

We have a survey with several questions with 5 likert scale points and we would like to compare the answers to those of another likert like question with 10 points. The approach we thought of is a ...
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0answers
18 views

how could i handle with missing data or non existent data? [duplicate]

i tried a forecasting method and i want to check if it is correct or not and why? my study is about evaluating mutual funds for two kind of them it is a comparative study and i wan to use gcc index ...
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16 views

Using t-test for feature selection after z-scoring data?

Suppose I have a high-dimensional dataset, and a binary classification problem. I want to use the two-sample t-test for feature selection. If the data has been normalized by z-scoring (so it has zero ...
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1answer
37 views

How to adjust data to remove influence of one or more features

For my first real data science project I would like to develop a model which better reflects review quality than "useful" votes. I am working with Yelp's latest Academic data set but this thinking ...
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0answers
22 views

Multicollinearity, feature selection for discriminant analysis and the error rate

I have a question regarding feature selection in LDA/QDA and deciding to eliminate variables to find an optimal model (lowest misclassification rate) I'm looking at how quadratic and linear ...
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1answer
64 views

Removing collinear variables for LDA/QDA in R

I'm new to R and I've been searching for a while for a function which can reduce the number of explanatory variables in my lda function (linear discriminant analysis). Basically, I've loaded the ...
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0answers
39 views

LASSO prediction model question

I am trying to create a prediction model with 33 predictors (brain metabolite levels in various regions) and 8 observations (cognitive test scores) with p>>n problem using LASSO in MATLAB (...
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0answers
63 views

Selecting a multiple linear regression model with categorical variables

I am trying to analyze the Berkeley Guidance Study to practice multiple regression models, which has 10 continuous variables, 1 categorical variable (with two categories) and the response variable. ...
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0answers
10 views

filter feature selection output and cross validation

If I use a filter method for ranking the features like Relief. suppose I have 100 features with 1000 sample and I used cross validation 3-fold . therefore I have 3 ranks for may features . at the end ...
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1answer
29 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
7 views

Combining features extracted from different parts of the same image

It is about car identification in images. I have an 64x64 image divided into 16 equal windows. I compute a HoG features algorithm in each one. And I am using the concatenation vector resulted from ...
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1answer
70 views

Tool form Hierarchical clustering

I'm trying to perform a hierarchical Clustering Analysis in a dataset of 40 attributes and +70,000 records, which is mostly composed by categorical variables. I've used Matlab and RapidMiner to ...
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0answers
25 views

Top K variable that represent entire dataset

There are 100 variables in the dataset. Also, i have extracted some additional information about each variable viz Var1 is correlated (Pearson correlation) to Var21,Var25,Var34,Var45,Var55 ; Var2 is ...
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22 views

Least-squares fitting with only optimum features, after Lasso - valid?

Using Lasso reduces the coefficients of features of a model, reducing some to zero, and thereby performing feature selection. The number of features depends on the value of $\alpha$ aka $\lambda$. In ...
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1answer
100 views

(Automated) feature selection in multiple regression with categorical variables

I need a general guide on what are the appropriate approaches to automated feature selection in multiple regression with categorical variables. In my case, I have several numeric and categorical ...
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0answers
19 views

Why normalized feature weights for linear regression are bad feature importance predictors

I am trying to interpret a linear regression model. I assumed using absolute value of feature weight coefficients as indicators of influence of input variable onto output variable. However, it seems ...
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1answer
73 views

Strategy for Analyzing Data

I have been learning about Machine Learning (via Udacity) and Statistics (via Coursera) the past few months and trying to figure out a good way to combine them for a general approach to explaining ...
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1answer
43 views

Feature selection when bagging trees/random forest

I want to get a better understanding of feature selection and how the number of features affect performance when bagging trees. I am using Matlab's treebagger and I ...
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2answers
126 views

Feature Selection: Information Gain VS Mutual Information

Setting: Multi-class classification problem with discrete nominal features. There are many references mentioning the use of IG(Information Gain) and ...
0
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1answer
6 views

Select the most confident variable that has two features

Suppose now I have a group of students, and for each student two measurements are given: one is the height of the student and the other is the weight of the student. Then my question is how I can ...
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0answers
47 views

Generate mixture model from data with features

I want to build a mixture model from my data, but using features of my data to calculate each component in the model. The data: For each point I have 34 associated features. Each feature is a boolean ...
0
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1answer
113 views

Weka java API: Attribute Selection and Cross Validation

Is there a way to perform Attirbute selection(aka feature selection) (regardless of method) only for the training dataset before passing data for Cross Validation ? I currently think that the only ...
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1answer
31 views

Use fitted value from regression on subset of features as independent variable

I am working with a relatively large data set with 2K columns and many variables can be grouped together (a logistic regression). So I am thinking can I use fitted value from regression on subset of ...
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0answers
17 views

Feature/Variable selection to accompany mixed models?

I am trying to conduct an exploratory/data mining analysis to discover what socioeconomic factors best predict grade-school performance in children. I have a dataset with about 50000 ...
0
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0answers
24 views

$\chi^2$ test vs F-test in feature selection

In the context of feature selection for classification, does it make sense to use one filter based on $\chi^2$ test and the other one based on F-test? Or they are "interchangeable"?
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1answer
40 views

mtry tuning given by caret higher than the number of predictors

According to this discussion, it seems that the train function of the caret package returns a ...