5
votes
2answers
114 views

Classification problem using imbalanced dataset

I am working on a pattern identification/classification problem on an imbalanced dataset, with target to non target proportion in population approx as 1%:99%. There are around 0.5 million records in ...
0
votes
0answers
45 views

Model selection for unbalanced data

How to do model selection for unbalanced data? how many data points from the whole data set should be selected for model selection? how many for training and testing?
2
votes
1answer
76 views

Outlier detection: at which degree of class imbalance would you consider a one-class model over a two-class model

Background: I am working on the problem of classifying objects found in some biological images. Time and again, we encounter objects which do not fall into any of the categories/classes we are ...
3
votes
2answers
127 views

Named entity recognition and class imbalance

I have implemented Maximum-entropy Markov model (MEMM) for the Named entity recognition (NER) problem. I have four classes: geographical, people, material (book titles etc) and other. Class ...
3
votes
1answer
480 views

Which performance measure for unbalanced binary classification without an 'active' class?

My datasets have two classes A and B. The classes should be treated equally (there is no "active/inactive"). The datasets are unbalanced, sometimes A is more frequent, sometimes B is more frequent. ...
9
votes
1answer
281 views

How to handle the difference between the distribution of the test set and the training set?

I think one basic assumption of machine learning or parameter estimation is that the unseen data come from the same distribution as the training set. However, in some practical cases, the distribution ...
6
votes
2answers
575 views

Classification with GBM in R and imbalanced class sizes

I'm dealing with a supervised binary classification issue. I'd like to use the GBM package to classify individuals as uninfected/infected. I have 15 times more uninfected than infected individuals. I ...
6
votes
1answer
579 views

A priori selection of SVM class weights

I remember seeing/reading somewhere that for multiclass SVMs with unbalanced data, there was a way to determine the class weights from the training data (rather than X validation). Does anyone know ...
9
votes
1answer
913 views

Optimising for Precision-Recall curves under class imbalance

I have a classification task where I have a number of predictors (one of which is the most informative), and I am using the MARS model to construct my classifier (I am interested in any simple model, ...
1
vote
1answer
2k views

Best way to handle unbalanced multiclass dataset with SVM

I'm trying to build a prediction model with SVMs on fairly unbalanced data. My labels/output have three classes, positive, neutral and negative. I would say the positive example makes about 10 - 20% ...
6
votes
2answers
768 views

Naive-Bayes classifier for unequal groups

I'm using naive bayes classifier to classify between two groups of data. One group of the data is much larger than the other (above 4 times). I'm using the prior probability of each group in the ...
7
votes
1answer
958 views

When over/under-sampling unbalanced classes, does maximizing accuracy differ from minimizing misclassification costs?

First of all, I would like to describe some common layouts that Data Mining books use explaining how to deal with Unbalanced Datasets. Usually the main section is named Unbalanced Datasets and they ...
5
votes
2answers
336 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
390 views

How to handle skewed binary target variables? [duplicate]

Possible Duplicate: Supervised learning with “rare” events, when rarity is due to the large number of counter-factual events I am trying to predict diabetes using the BRFSS ...