0
votes
0answers
16 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
votes
0answers
18 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"?
0
votes
0answers
9 views

Unsupervised feature learning from raw text as a previous step for clasification?

I have a corpus of 2500 opinions, is it posible to use scikitĀ“s restricted boltzmann machine implementation to extract a feature vector as a previous step to a classification task?. What aproach do i ...
1
vote
0answers
18 views

Feature boosting via rescaling in logistic regression and linear SVMs

If I were expressing a problem in terms of binary features, all encoded as {0,1}, could I boost some features by encoding them as {0,2}? Would the effect change based on whether I used either of the ...
0
votes
0answers
33 views

different feature types for classification

There has a data set with several features. One feature is of the type of continuous numerical values; another feature is of the type of categorical values, such as A, B and C. If I want to build a ...
0
votes
0answers
22 views

How to deal with a feature relating to what type of expert labelled the data that becomes unavailable at point of classification?

Essentially I have a data set, that has a feature vector, and label indicating whether it is spam or non-spam. To get the labels for this data, 2 distinct types of expert were used each using ...
2
votes
4answers
94 views

Interpreting conflicting results from Random Forest & Logistic Regression?

I am using SKLearn and Statsmodel in python to build a RF and Logistic Regression, respectively. I have a feature that the RF indicates is important (feature importance of 0.202, closely behind #1 ...
0
votes
1answer
29 views

mixing binary and real-valued features with SGD

I'm going to be using a logistic regression model and using SGD to determine the feature weights. Is it OK for me to use a mix of binary and real features, without doing anything like scaling or ...
0
votes
1answer
14 views

Feature selection with a binary dependent variable

Given we have a binary dependent variable and 100s of features and ~50k observations, is there a generally accepted way to trim the features via some type of machine learning concept? I was trying a ...
0
votes
0answers
10 views

When to cluster features for supervised learning?

I'm doing a project on dog adoption patterns, and I realized that there are many (100 +) different breeds of dogs. I'd like to build a predictive model using breed as covariate, but I'm not sure ...
0
votes
0answers
5 views

Design a feature with time and presence information

Context: I am working on a decision tree classifier, trying to classify businesses as to whether they are likely to have an event occur (default) in the next 90 days. One input I get is whether, and ...
1
vote
0answers
17 views

How to assess the importance of the features which come from intersection of features of the two models?

I have two models from two different data sets. Model 1 contain 50 features and model 2 contain 40 features. the intersection of features of model 1 and 2 is 10. so how can I assess the relative ...
1
vote
1answer
28 views

Feature Selection - Mutual Information with response variable that takes three values

I am trying to calculate Mutual Information scores for Feature Selection. I have successfully implemented the Mutual Information to test each feature against the binary response variable. Each ...
0
votes
0answers
25 views

How to interpret merits in Weka with ChiSquaredAttributeEval and SVMAttributeEval?

I want to interpret the goodness of attributes using feature selection with 10-fold cross validation. With ChiSquared I get something like this (deletet attributes with merrit was 0 in all folds): ...
1
vote
1answer
92 views

Maximum Entropy Model for classification, what to use as context & feature?

I'm building a Maximum Entropy Model to classify some text, based on paper "A Maximum Entropy Approach to Natural Language Processing" by Berger et.al. It's similar to POS tagging. Below is some ...
1
vote
1answer
120 views

Feature selection : how to select the Information Gain threshold?

I am trying to use Information Gain to select features when classifying text with a Support Vector Machine. For each word in our training data, we computed its information gain. Then, we should keep ...
2
votes
2answers
97 views

Model Tuning and Model Evaluation in Machine Learning

Despite my readings (on stack 1, 2, or in literature (Cawley, 2010; Japkowicz, 2011)), I don't find a clear procedure for tuning and evaluating a model in a classification task. I want to perform a ...
0
votes
2answers
79 views

Best feature selection method for naive Bayes classification

i want to make classification with naive Bayes. I have got about 100 Features. Numerical ones as well as categorical ones. Since i want only the most relevant ones to be included for the ...
0
votes
1answer
23 views

Importance of Time Features

if you have a time series and you want to do some predictions, what time feature should you use ? lets say we are trying to predict how many people visit a certain website, we have data for the ...
0
votes
0answers
45 views

What method should I use for this optimization / feature selection project

I'm going to describe a problem and I'm not sure how to best solve it. I will describe the situation. When answering please recommend a method and maybe a software library. I'm using Python for my ...
0
votes
1answer
53 views

Interpreting results of a factor analysis

I performed factor analysis on R using factanal. Following advice I found on this tutorial, I chose the number of factors as being the number of principal components that capture 90% of the ...
1
vote
1answer
42 views

Feature selection based on information gain papers

I want to apply feature selection based on information gain: I have many features many of which are redundant. I am planning on selecting a feature and then iteratively add features that 'add the more ...
0
votes
0answers
54 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 ...
3
votes
3answers
293 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 ...
2
votes
1answer
47 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, ...
1
vote
0answers
67 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 ...
0
votes
1answer
15 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, ...
2
votes
1answer
33 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 ...
0
votes
0answers
43 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 ...
0
votes
1answer
25 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 ...
0
votes
0answers
217 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 ...
0
votes
1answer
129 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 ...
1
vote
0answers
41 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 ...
2
votes
1answer
69 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 ...
3
votes
1answer
101 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 ...
0
votes
0answers
20 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 ...
0
votes
1answer
155 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 ...
0
votes
1answer
45 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 ...
3
votes
3answers
52 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
votes
0answers
31 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 ...
0
votes
1answer
117 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 ...
3
votes
1answer
685 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 ...
0
votes
0answers
73 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 ...
5
votes
1answer
160 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 ...
2
votes
2answers
120 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, ...
0
votes
3answers
92 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 ...
0
votes
1answer
161 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 ...
0
votes
0answers
20 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 ...
1
vote
0answers
73 views

How to explain difference of importance between feature selection and model quality?

I have a data collection with a mixed feature set consisting of both numerical features and text features. The number of numerical features is quite small, i.e., 6, comparing to the number of text ...
0
votes
1answer
38 views

Predictive features with high presence in one class

I am doing a logistic regression to predict the outcome of a binary variable, say whether a journal paper gets accepted or not. The independent variable or predictors are all the phrases used in these ...