1
vote
0answers
20 views

Setting up feature vectors

I am working on a classification project and I want to use SVM's and/or Clustering Algs. What I am having trouble with is deciding how to set up my feature vectors. I have already decided what my ...
0
votes
0answers
9 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
51 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 ...
0
votes
0answers
6 views

Nonlinear functions of other features as new features in SVM model with RBF kernel

Can some one give me some conceptual insight on the potential advantages of disadvantages of adding features that are (nonlinear) functions of existing features in training an SVM model with an RBF ...
1
vote
0answers
23 views

Unstable models, repeated crossvalidation, feature selection

I'm still trying to classify few (about 200) samples in a high dimensional feature space (dim=19) into 3 (very unbalanced) classes. I use an implementation of Least Squares SVM with one vs one coding ...
1
vote
0answers
24 views

why feature scaling or weighting is important in surpervised learning?

I can understand feature scaling or weighting is important in unsurpervised learning case, because we want an good representation of "similarity". But why it is also important in surpervised learning ...
1
vote
0answers
15 views

Is linear kernel SVM performance between features indicative of RBF kernel SVM performance?

I have feature set 1 and feature set 2. If a linear kernel SVM performs better ("better" meaning greater classification accuracy) when using feature set 1, does this guarantee that a properly tuned ...
0
votes
0answers
14 views

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 ...
0
votes
0answers
37 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
0answers
244 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). ...
0
votes
1answer
68 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, ...
3
votes
1answer
91 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
42 views

Feature selection for support vector regression with time series as features

I would like to select features for a support vector regression for forecasting. I would like to forecast a value at point t with the values t-1,...t-x as features. Now I want to select the most ...
0
votes
0answers
71 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 ...
1
vote
2answers
124 views

The curse of dimensionality? (linear SVMs)

How do you know whether you suffer from it? Let's suppose I have a 2 class problem - 2000 training examples and 30 features. While it works good for the most part, sometimes I get edge cases that ...
1
vote
1answer
38 views

Algorithms/methods to create more features of a limited amount of features?

So, let's suppose that I have a set of 20 features - some of them are continous and some of them are binary. Is there an algorithm/method/solution to create more features ( combine/transform ) those ...
1
vote
1answer
38 views

SVM - combining binary and continous representation of the same feature?

How would this influence the accuracy of the SVM model? Let's suppose that I have one variable which max value is 100 and minimum is 0. Currently, I send it to SVM as a single continuous feature, ...
1
vote
2answers
239 views

Mixing continuous and binary data with linear SVM?

So I've been playing around with SVMs and I wonder if this is a good thing to do: I have a set of continuous features (0 to 1) and a set of categorical features that I converted to dummy variables. ...
0
votes
1answer
140 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 ...
2
votes
3answers
387 views

How to divide feature set for selection and training

I have training data with 260 observations that have a total of 7 classes. Each observation has 120 features. I applied feature selection based on the Bhattacharyya Algorithm and got the top 40 ...
2
votes
0answers
243 views

Feature importance scores of SVM multiclass one-vs-one design

Info about dataset: 5 classes, 200 trials, 100 features. (I know about the trial to feature ratio being very low, but can not avoid this here and still got well enough classification results.) ...
0
votes
3answers
257 views

Feature selection before SVM

I have a simple but difficult question. Does feature selection before SVM help? I have a data set that has ~1100 features but a lot of these are redundant data / uncorrelated data. Can someone give me ...
1
vote
2answers
129 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
vote
0answers
33 views

Performance worse with new observations

I come from the computer science area but am new to machine learning / stats, so this question may be fundamental and easy. I have time-series data (biological data), and, without getting into the ...
3
votes
1answer
195 views

Is it possible to compare two feature selections algorithms by cross-validations?

Assume I have two feature selection algorithms, A and B, which are developed based on SVM. I applied these two algorithms on the same dataset, a Liver Cancer dataset (400 features & 150 samples), ...
0
votes
0answers
218 views

Issues with sequential feature selection

I am trying to do some feature selection in gene expression data with 22215 features. I followed the tutorial here. I initially applied filter method(ttest) to select the features having the best p ...
3
votes
1answer
573 views

Using Adaboost for feature selection?

Is it okay to use Adaboost to do feature selection (selecting a subset of dimensions $S$ from a high-dimensional feature vector $V$)? I divided the samples into four non-overlapping sets: $A$ ...
1
vote
1answer
724 views

How does scikit-learn perform $\chi^2$ feature selection on non-categorical features?

I'm experimenting with $\chi^2$ feature selection for some text classification tasks. I understand that $\chi^2$ test checks the dependencies B/T two categorical variables, so if we perform $\chi^2$ ...
2
votes
1answer
2k 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 ...
2
votes
0answers
226 views

Kernel in PenalizedSVM R package

There is not option to select kernel in penalizedSVM R package. What kernel do they use? Is there some other R package with penalized SVM methods where I can choose various kernels?
2
votes
1answer
186 views

Multiclass classification with SVM a question about the feature vectors

I was told I had to direct my machine learning questions to this site. So here it goes. I'm trying to do Multiclass classification with SVM. I have 7 classes. Now I was wondering if the following is ...
3
votes
3answers
5k views

How does one interpret SVM feature weights?

I am trying to interpret the variable weights given by fitting a linear SVM. (I'm using scikit-learn): ...
7
votes
4answers
1k views

Low classification accuracy, what to do next?

So, I'm a newbie in ML field and I try to do some classification. My goal is to predict the outcome of a sport event. I've gathered some historical data and now try to train a classifier. I got around ...
1
vote
0answers
225 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, ...
15
votes
3answers
238 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 ...
4
votes
1answer
597 views

SVM for Image Segmentation?

I turn to this forum for advice with the following problem. If you could please shed some light on any aspect of this question I'd be very grateful. Problem decription: I'm trying to use an SVM to ...
1
vote
1answer
249 views

SVM importance of predictor variables

I am building a model in R using support vector machine (SVM) with KBF kernel. The model seems to work quite well. I would like to assess the relative importance of predictor variables. Can anyone ...
3
votes
1answer
571 views

How to select the final model with elastic net feature selection, cross validation and SVM?

I have a dataset of some 100 samples, each with >10,000 features, some of which highly correlated. Here's what I am doing currently. Split the data set into three folds. For each fold, 2.1 Run ...
2
votes
0answers
126 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 ...
2
votes
1answer
775 views

The disadvantage of using F-score in feature selection

F-score can be used to measure the discrimination of two sets of real-numbers and can be used for feature selection. However, I once read that A disadvantage of F-score is that it does not reveal ...
9
votes
3answers
3k views

Improving the SVM classification of diabetes

I am using SVM to predict diabetes. I am using the BRFSS data set for this purpose. The data set has the dimensions of $432607 \times 136$ and is skewed. The percentage of ...
17
votes
2answers
2k views

Variable importance from SVM

How to obtain a variable (attribute) importance using SVM?