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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527 views

Correlation and group difference with unequal sample sizes

My sample is 60 participants. At first, I did not expect to analyze the correlation, but the result is interesting, so I would like to. The 60 participants are 50 people who have good reading skill ...
3
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1answer
87 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 ...
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0answers
178 views

How to set parameters for SMOTE in WEKA?

How I can choose the most optimal parameters for SMOTE in WEKA. More specifically, how should I pick the nearest-neighbours and the SMOTE to apply? I have been using the amount of SMOTE that gives the ...
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2answers
2k 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 ...
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0answers
114 views

Questionnaire analysis and grouping responses

I want to know if the creative process engagement (CPE) 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) ...
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1answer
256 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 ...
3
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0answers
183 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 (...
7
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2answers
787 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, ...
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0answers
71 views

How do you analyse an unbalanced dataset using dummy variables?

I have a returns (finance) dataset with 3 factors, each with 3 levels: industry (banking, retail, others), year (2010, 2011, 2012), and size (small, medium, large). Each factor has been coded as a ...
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0answers
125 views

Interpreting coefficients for random effects models with extremely unbalanced data

I'm currently working with a data set that has numerous samples collected over time at different sites in a study area, and I'm interested in detecting a trend over time for that area. I know that in ...
3
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1answer
53 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 ...
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2answers
83 views

Choosing the best featureset for prediction

I have this N sets of features F each with $F_i$ number of features. All the feature sets have 20000 examples and we have 20,000 labels for them. Lets say feature set $F_1$ has 10 features and ...
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1answer
82 views

Finding the best dataset for classification

I have 100 datasets. All of them have varying number of features. There are around 20,000 samples in each of them. Every $i$-th sample in the 100 datasets has the same label ($0/1$). The data is ...
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2answers
331 views

How similar are my 2 data sets?

I am kind of stuck with an easy question: I have two data sets with experimental data. The data sets do not have the same size. I would like to show that these data sets are possibly coming from the ...
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0answers
28 views

Impact of biased sampling on classifier training ?

Let's imagine I have a very unbalanced dataset with 99.99% of 0 and 0.01% of 1 on the target variable. What I want to do is make a classifier for this target. Now imagine that this dataset is very ...
5
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2answers
385 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 ...
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0answers
62 views

Sample size for binary text classification

In my application as a Binary text classification, one class has around 36,000 sample and another one has around 300 samples. I under-sample the first class. So, each class will have ~ 300 samples. ...
3
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1answer
317 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
829 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
288 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
160 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: ...
4
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1answer
473 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
111 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
239 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. ...
2
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1answer
335 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
286 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
711 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: ...
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1answer
70 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 ...
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2answers
405 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
111 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
289 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
230 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). ...
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0answers
261 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 ...
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1answer
1k 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
903 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
156 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
200 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 ...
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1answer
1k 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: ...
3
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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
103 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 ...
4
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1answer
112 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 ...
5
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3answers
260 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), ...
10
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1answer
532 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
191 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 ...
6
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1answer
486 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 ...
7
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2answers
958 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
299 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
902 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
222 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 ...