0
votes
0answers
18 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
40 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
29 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
66 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
29 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
38 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
66 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
35 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
35 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
88 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
124 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
224 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
206 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
214 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
1answer
106 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
29 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
179 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
142 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
490 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
593 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
1k 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
194 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
169 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
4k 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
197 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
228 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
493 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
225 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 ...
2
votes
1answer
498 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
117 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
696 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?