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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486 views

Dataset of repeated measures with unbalanced sample size and unequal variace - What to do?

I have a dataset of three groups of cells treated with 10 different compounds and am not sure how to check for significant differences between those treatments. Within each group the data is also ...
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1answer
191 views

Imbalanced data classification using boosting algorithms

I am working on a binary data classification problem. The dataset is imbalanced, it consists of 92% 'false' labels and 8% 'true' labels. The number of features is 18 and I have a small number of 650 ...
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1answer
107 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: ...
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1answer
356 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 ...
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1answer
96 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 ...
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0answers
186 views

Repeated measures ANOVA for an experiment with missing values

I have an experiment where several subjects (subjects $= S_1,S_2,...,S_m $) were asked to perform a set of tasks (tasks $= T_1, T_2, T_3,...,T_n$) using both their left ($L$) and right ($R$) arms. ...
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1answer
243 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 ...
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2answers
244 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 ...
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2answers
470 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: ...
2
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1answer
67 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
264 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 ...
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0answers
95 views

How to create the class range given 1 as the middle class, 3 as the highest value and 0.65 as the lowest value?

Good day. I know getting the class interval given 3 as the highest value and 0.65 as the lowest value is easy. Here's the catch, the distribution of the interval starts at 1 which is considered as the ...
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1answer
218 views

How to balance classification?

I have a binary classification problem, where my training data is 70% positive labeled and 30% negative labelled. I use a logistic loss and it always classifies examples positive on the test data. ...
3
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0answers
187 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). ...
4
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0answers
166 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
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1answer
744 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. ...
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1answer
637 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 ...
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0answers
79 views

Type I ANOVA tests not depending on the order of the factors

I have a dataset with two factors A and B and the following design (contigency table showing the number of individuals for each ...
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2answers
146 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 ...
2
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0answers
180 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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1answer
783 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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2answers
3k 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: ...
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0answers
93 views

Different Mean Square partitions in an unbalanced bifactorial ANOVA (with random factor) between R and Statistica

I am trying to extract variance components for selection and chance in a bifactorial design with Generation as a fixed factor and Replicate as a random term, for early fecundity. Since I am using ...
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1answer
107 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 ...
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3answers
223 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), ...
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1answer
387 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 ...
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0answers
180 views

How to compare multiple groups of unblanced repeated measures non normal data?

I'm trying to compare three groups data. But the data set is about a new drug trial. The data set has these characteristics: Follow-up set. That is, after administration of the drug, a series of ...
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1answer
441 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 ...
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2answers
776 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 ...
0
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1answer
270 views

How to handle data imbalance in Principal Component Analysis?

PCA reduces data set dimensions while trying to keep most variations in data set. PCA can be used as a dimension reducing technique in discrimination, however it tries to keep the most discrimination ...
2
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0answers
787 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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1answer
189 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 ...
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2answers
3k 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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1answer
693 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 ...
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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 ...
2
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1answer
430 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. ...
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1answer
1k 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, ...
3
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0answers
224 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 ...
4
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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% ...
5
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2answers
1k 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 ...
6
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2answers
946 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 ...
2
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0answers
513 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 ...
9
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1answer
1k 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 ...
2
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1answer
246 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 ...
5
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2answers
372 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 ...
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1answer
433 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 ...
5
votes
3answers
718 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. ...
2
votes
2answers
403 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?