Data organized into discrete categories or *classes* may present problems for certain analyses if the number of observations ($n$) belonging to each class is not constant across classes. Classes with unequal $n$ are *unbalanced*.

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275 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 ...
9
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1answer
909 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, ...
7
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2answers
184 views

Does a big difference in sample sizes matter for an independent t-test?

There is a very confusing question in my mind. I have data, and would like to compare numeric scores between men and women. There is a big difference in those two groups: the number of men is 34, ...
7
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1answer
953 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 ...
6
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2answers
2k views

Training a decision tree against unbalanced data

I'm new to data mining and I'm trying to train a decision tree against a data set which is highly unbalanced. However, I'm having problems with poor predictive accuracy. The data consists of students ...
6
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2answers
764 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 ...
6
votes
1answer
574 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 ...
6
votes
1answer
395 views

CART (rpart) balanced vs. unbalanced dataset

I am fitting a tree (CART) to the olives-dataset. The training data has 436 observations (test data: 136). I have 3 responses (the 'Region' variable) which splits the training data into 116 / 74 / 246 ...
6
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2answers
566 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 ...
5
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3answers
181 views

Experiment design: Can unbalanced dataset be better than balanced?

I'm on the stage of experiment design of some biomedical time-course study. Let's say we will have 2 groups of subjects - case and control. The total number of subjects is limited (for example, 30), ...
5
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2answers
334 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 ...
5
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3answers
691 views

Might be an unbalanced within subjects repeated measures?

I ran a within subjects repeated measures experiment, where the independent variable had 3 levels. The dependent variable is a measure of correctness and is recorded as either correct / incorrect. ...
5
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2answers
956 views

GLM on unbalanced design

I have a dataset that comprises 200 males and 250 females and I am testing their responses on the relationship between X and Y. X and Y are continuous and X1 (gender) is categorical. I am using ...
5
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2answers
113 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 ...
4
votes
1answer
229 views

Is a large control sample better than a balanced sample size when the treatment group is small?

I am running an experiment looking at brain volume changes in a rare disorder. We have a small number of patients (n = 8) but a large control group (n = 100). Some colleagues have suggested that a ...
4
votes
1answer
102 views

Logistic regression without negative samples

I have a data set of RNA reaction values of breast cancer. I want to figure out which RNAs are essential genes by Logistic Regression & LASSO. The data set has no negative samples. What should I ...
4
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0answers
131 views

Mixed-effects model for a strongly unbalanced design

I am somehow unsure on the best option to analyze these data. Here is my study case: The response variable is a morphometric measure, one for each individual. During 10 years, say 2000-2009, people ...
3
votes
3answers
1k views

SVM vs. artificial neural network

I have multiclass unbalanced data (4 class with 15% 25% 45% 15% data in each class). Which method is good for classification of such data- SVM or ANN? UPDATE- Let me make the question little more ...
3
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2answers
216 views

Calculating statistical significance with unequal sample sizes and unequal variances

I have two samples, one with $n_1 = 41,000$ and the other with $n_2 = 881$; the larger sample has a standard deviation of $13.74$, and the smaller has an $SD=10.75$. The means are different, and when ...
3
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1answer
52 views

Using Wilcoxon-Mann-Whitney test for comparing two population of different sizes

I am using Wilcoxon-Mann-Whitney test for comparing two populations. Unfortunately, sizes of my population are different; one has size 100000 and the other 6000. Can I use this test to compare these ...
3
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2answers
126 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
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1answer
39 views

Cross Validation in Unbalanced Datasets

Is there a specific way of sampling which maintains the ratio of samples in an unbiased set? e.g., lets say I want to do k-fold cross-validation on my training set And my training set is very ...
3
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1answer
48 views

What test of significance should I use to analyse visits to a clinic by various groups?

Apologies for the noob question. After a day on Google and Wikipedia I still can't quite work out what to do. So here I am. A small private healthcare clinic in the UK has asked me to look at their ...
3
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1answer
472 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. ...
3
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0answers
140 views

Mixed ANOVA: small and unbalanced samples

I have to analyze two samples ($n_1=10, n_2=18$) in a design in which there is a between-subject factor (Groups: 2 levels) and a within-subjects factor (...
3
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0answers
136 views

Problem with classifier after using SMOTE to balance the data

We've ran into a problem while training a classifier on an unbalanced data set. The response is binary with 0 indicating 'non defaulter' and 1 indicating 'defaulter' (it's a credit scoring task). ...
3
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0answers
209 views

Softmax regression bias and prior probabilities for unequal classes

I'm using Softmax regression for a multi-class classification problem. I don't have equal prior probabilities for each of the classes. I know from Logistic Regression (softmax regression with 2 ...
2
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2answers
368 views

kNN and unbalanced classes

Do you think that unbalanced classes is a big problem for k-nearest neighbor? If so, do you know any smart way to handle this?
2
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2answers
2k views

LibSVM cost weights for unbalanced data doesn't work

I have a dataset where the number of negative labeled values is 163 times the number of positive labeled values. That is, I have an unbalanced data set. I tried: ...
2
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1answer
605 views

Balanced accuracy vs F-1 score

I was wondering if anyone could explain the difference between balanced accuracy which is b_acc = (sensitivity + specificity)/2 and f1 score which is: ...
2
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1answer
386 views

Are there problems with inference using linear regression on observational data with highly skewed distributions of predictor values?

I am using a linear regression model to perform inference on some observational data. The samples are from an observational study and highly skewed along some of the dummy variables in the regression. ...
2
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1answer
240 views

Imputation with panel data exhibiting dependence structure

Let's say that we have longitudinal panel data. Rows are unique by date and individual. Columns consist of characteristics of the individuals on the given date as well as a dependent variable. My ...
2
votes
1answer
75 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 ...
2
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1answer
175 views

SMOTE throws error for multi class imbalance problem

I am trying to use SMOTE to correct imbalance in my multi-class classification problem. Although SMOTE works perfectly on the iris dataset as per the SMOTE help document, it does not work on a similar ...
2
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1answer
62 views

Looking for simple examples of how to calculate type III and type IV SS

I have data collected on five species of fish at half a dozen locations in a lake over four years. The categories are not (at all) fully crossed, and I have a lot of empty cells due to logistical ...
2
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1answer
166 views

Proper order of variables in unbalanced ANOVA

If you have unequal sample sizes in cells, then the order in which you enter model terms changes your results for sequential or Type I SS. The first variable to enter the model is allocated its ...
2
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1answer
48 views

unbalanced samples random Forests

I am trying to predict species presence or absence using randomForest in R (classification). In fact, I am trying to do it for several species, in separate models. For a couple of the species, the ...
2
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0answers
157 views

How to use k nearest neighbours for binary classification with unbalanced classes?

I have relatively large (100k items) dataset which I need to split in two groups. So far I've tried knn and the results are not good mainly because I have disproportion in my training data: 90% of ...
2
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0answers
701 views

Using AdaBoost on multi-class in R on unbalanced data

I have a data set which is highly imbalanced and I have used the SMOTE algorithm (using the R package DMwR) to balance the binary class in the data set. I have been using the R Ada package to then ...
2
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0answers
481 views

How to analyze an unbalanced within-subject ANOVA design?

I have data from an experiment involving 4 groups of subjects, 2 possible interventions first and 3 possible intervention at a 2nd point, repeated data measurements from each subjects multiple time ...
1
vote
1answer
80 views

Denominator is Zero for Matthews correlation coefficient and F-measure

Recently, I built a classification model based on the imbalanced data set(positive sample is minority and negative sample is majority), and the model gave the following result for the test set: ...
1
vote
1answer
185 views

Testing Classification on Oversampled Imbalance Data

I am working on severely imbalanced data. In literature, several methods are used to re-balance the data using re-sampling (over- or under-sampling). Two good approaches are: SMOTE: Synthetic ...
1
vote
2answers
135 views

Can I run a GLMM model when I have one observation for most subjects?

I have a binary DV and my panel data set contains more than one observation for only 20% of the subjects which makes it very unbalanced. Is there anything methodologically wrong with doing a mixed ...
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% ...
1
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2answers
23 views

Collecting training data for document classification with unbalanced classes

I have a document classification problem in which the estimated class proportions in the population are severely unbalanced: the population is ~99% class 0 and ~1% class 1. I am using a logistic ...
1
vote
1answer
71 views

How to analyze unequal samples in linear and multiple regression

I have three variables (sample size is mentioned below), and I want to analyze data using regression models as recommended by Judd and Kenny (1981) to see if $cc$ mediates the relationship between ...
1
vote
1answer
78 views

classification threshold in RandomForest-sklearn

1) How can I change classification threshold (i think it is 0.5 by default) in RandomForest in sklearn? 2) how can I under-sample in sklearn? 3) I have the following result from RandomForest ...
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2answers
320 views

By using SMOTE the classification of the validation set is bad

I want to do classification with 2 classes. When I classify without smote I get: ...
1
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1answer
406 views

How to set SMOTE parameters in R package DMwR?

For different imbalanced data-sets which rare class' proportion differ from 30% (rare) to 5% (rare), what is the best way to define the Perc.Over and ...
1
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1answer
25 views

Questionaire analysis and grouping responses

I want to know if the creative process engagement is related to creativity. I don't know how to analyze the questionnaire. I will be collecting data for cpe with Zhang and Bartal's (2010) 11-item ...