Tagged Questions
1
vote
1answer
29 views
Features selection using F-score for multiclass classification
I'm going to implement a feature selection algorithm, and I plan to use the F-score for because of its simplicity. The problem is that, the F-score is used for binary classification. How can it be ...
1
vote
1answer
30 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 ...
2
votes
2answers
118 views
Mutual Information based feature selection
Suppose I have a feature matrix $F = [f_1^T,f_2^T,...,f_m^T]$ whereby $f_j^T \in \mathbb R^{n_t \times 1}$ is the $j$th column vector of $F$ ($n_t$ is the number of different events/trials and $m$ is ...
3
votes
1answer
65 views
Variable importance randomForest negative values
I am asking myself if it is a good idea to remove those variables with a negative variable importance value ("%IncMSE") in a regression context. And if it gives me a better prediction? What do you ...
2
votes
1answer
207 views
What is “feature space”?
What is the definition of "feature space"?
For example,
When reading about SVMs, I read about "mapping to feature space".
When reading about CART, I read about "partitioning to feature space".
I ...
1
vote
0answers
66 views
Univariate feature ranking in classification
Scikit-learn has function to evaluate the F-statistics for univariate feature importance feature selection. According to the web page they are calculating ANOVA F value.
If I understood correctly, ...
4
votes
2answers
483 views
Number of trees for Random Forest optimization using recursive feature elimination
How many trees would you suggest to pick to perform recursive feature elimination (RFE) in order to optimize Random Forest classifier (for binary classification problem). My dataset is very ...
0
votes
0answers
99 views
What are the good algorithms for feature extraction for large dataset?
I have KDD dataset for detecting fraud actions on networks but it has millions of lines and >20 feature columns. Thus it is not viable to process all these on my personal computer. I am thinking about ...
4
votes
5answers
363 views
Is using the same data for feature selection and cross-validation biased or not?
We have a small dataset (about 250 samples * 100 features) on which we want to build a binary classifier after selecting the best feature subset. Lets say that we partition the data into:
Training, ...
4
votes
6answers
392 views
What machine learning algorithms are good for estimating which features are more important?
I have data with a minimum number of features that don't change, and a few additional features that can change and have a big impact on the outcome. My data-set looks like this:
Features are A, B, C ...
0
votes
1answer
115 views
R package for feature set algorithm selection
I want to train a binary classification NN and part of this will require data pre-processing. However, I have a choice of which pre-processing algorithm to use. Of course I'd like to choose that one ...
0
votes
0answers
128 views
Feature selection for SVM and Maximum Entropy
In text classification problems where the number of features >> number of documents, is it useful to perform feature selection with filters (e.g. Information Gain) when using Naive Bayes. However, ...
5
votes
1answer
455 views
How to avoid overfitting when using crossvalidation within Genetic Algorithms
This is a long set-up, but the pure intellectual challenge will make it worthwhile I promise ;-)
I have marketing data where there is a treatment and a control (i.e a customer gets no treatment). The ...
12
votes
3answers
182 views
Is building a multiclass classifier better than several binary ones?
I need to classify URLs into categories. Say I have 15 categories that I'm planning to zero down every URL to.
Is a 15-way classifier better? Where I have 15 labels and generate features for each ...
3
votes
1answer
88 views
How to determine significant subgroups of data inputs? [duplicate]
I have a large $(10000 \times 5001)$ table representing $10000$ samples and $5001$ different features of these samples. One of these features represents an output variable of each sample. In other ...
6
votes
2answers
631 views
Finding the best features in interaction models
I have list of proteins with their feature values. A sample table looks like this:
...
1
vote
0answers
133 views
Guassian Process Regression - feature selection
I'm using guassian process regression to do some modeling. One issue I'm encountering is feature selection for some of my models, which often have many relevant features. I'm not sure what the best ...
3
votes
1answer
391 views
Feature selection methods for document classtification
I have a simple document classification problem where i need to classify some documents to a definite set of classes.
I need to perform a feature selection (where I will select the most important ...
1
vote
1answer
158 views
Feature selection weighting 2 filters in Naive Bayes
I am trying to do text classification using Naive Bayes. Before training, I would like to make feature selection in order to reduce the feature space dimension. In order to do so, I have thought of ...
1
vote
1answer
104 views
Is there a rule of thumb on the relationship between the number of instances and the number of features?
If we build a classifier based on a very small number of instances (say, fewer than 300) and the number of features we are using is very large (say, larger than 100k features). If we decide to ...
4
votes
2answers
333 views
Feature selection for the text mining?
Before performing the task of text mining, we need to select the features for characterizing each given document. Are there any systematic guidance on choosing the document features? How does the ...
2
votes
1answer
224 views
Open source implementation elastic net in C or C++
Can anyone provide or point me to a freely available implemention of Elastic Net in C or C++?
3
votes
4answers
524 views
How to know when to stop reducing dimensions with PCA?
I'm using PCA to reduce dimensionality before I feed the data into a classifier. My bootstrap/cross-validation has shown a significant reduction in test error as a result of applying PCA and keeping ...
2
votes
0answers
81 views
Non-linear (e.g. RBF kernel) SVM with SCAD penalties implementation
Is there one? I think there's a penalizedSVM package in R but it looks to use a linear kernel. Can't quite tell from the documentation. If it's linear, is there a R package that lets me calculate the ...
6
votes
1answer
198 views
Dealing with very large time-series datasets
I have access to a very large dataset. The data is from MEG recordings of people listening to musical excerpts, from one of four genres. The data is as follows:
6 Subjects
3 Experimental repetitions ...
2
votes
2answers
55 views
4
votes
2answers
262 views
Feature selection for low probability event prediction
I'm currently trying to predict the probability for low probability events (~1%).
I have large DB with ~200,000 vectors (~2000 plus examples) with ~200 features.
I'm trying to find the the best ...
1
vote
1answer
190 views
Variable selection for increasing accuracy
I know that there are various posts regarding variable selection but I am asking something particular. With respect to the question that I posted today in the following link:
Low accuracy in out of ...
2
votes
1answer
176 views
Interpretation of “one” feature change in a supervised classifier
i'm making experiments using app. 5000 labeled dataset.i'm trying different supervised ML algorithm to evaluate the results.The vector size is 13 with the labels (totally 12 features+1 label) and i ...
9
votes
2answers
404 views
Best methods of feature selection for nonparametric regression
A newbie question here. I am currently performing a nonparametric regression using the np package in R. I have 7 features and using a brute force approach I identified the best 3. But, soon I will ...
2
votes
1answer
61 views
Analysis of variables of varying numbers
I work with amino acid sequences and I want to use a self-made model to tell me something about it, lets call it $f(\text{seq})$. Now i want to know the contribution of every position in the sequence ...
23
votes
6answers
2k views
Feature selection for “final” model when performing cross-validation in machine learning
I am getting a bit confused about feature selection and machine learning
and I was wondering if you could help me out. I have a microarray dataset that is
classified into two groups and has 1000s of ...
15
votes
2answers
1k views
9
votes
4answers
759 views
Application of machine learning techniques in small sample clinical studies
What do you think about applying machine learning techniques, like Random Forests or penalized regression (with L1 or L2 penalty, or a combination thereof) in small sample clinical studies when the ...
12
votes
6answers
1k views
Variable selection procedure for binary classification
What are the variable/feature selection that you prefer for binary classification when there are many more variables/feature than observations in the learning set? The aim here is to discuss what is ...
6
votes
6answers
1k views
Algorithms and methods for attribute/feature selection?
I have data with continuous class and I'm searching for good methods to reduce number of attributes. Now I'm using correlation based filters, random forests and Gram–Schmidt algorithm.
What I want ...
