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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Trial with two sites, two years - Different number blocks - Problem? (Q1)

I have got two questions on an agricultural field trial that was conducted at two sites in two conscutive years. Virtually everything was the same in all trials (crop variety, planting density...). ...
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34 views

SVM for unbalanced data

I want to attempt to use Support Vector Machines (SVMs) on my dataset. Before I attempt the problem though, I was warned that SVMs dont perform well on extremely unbalanced data. In my case, I can ...
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2answers
25 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 ...
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10 views

How do I partition variance among nested random effects for non-normal data and an unbalanced design?

I have a dataset of plant drought tolerance values (called TLP_DRY) that I would like to partition variance for among the nested levels Biome/Study site/Species to figure out whether most variation in ...
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1answer
23 views

Should I specify Prior or Cost matrix with Tree Bagger in Matlab

I'm trying to create Random Forests in Matlab and there are more observations in some classes than there are in others. Do I need to specify this as a cost matrix or as a prior probability or will ...
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1answer
56 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 ...
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1answer
52 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 ...
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2answers
76 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 ...
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1answer
46 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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82 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
232 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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1answer
27 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 ...
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1answer
73 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 ...
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0answers
144 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 (...
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2answers
189 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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31 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
54 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 ...
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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 ...
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2answers
53 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
46 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
91 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
25 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 ...
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2answers
115 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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33 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. ...
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1answer
79 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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46 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?
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1answer
225 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
81 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
234 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
77 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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134 views

Repeated measures ANOVA for an experiment with missing values (unbalaced)

I have an experiment where several subjects (subjects={S1,S2,..Sm})were asked to perform a set of tasks (tasks={T1, T2, T3, ..Tn}) using both their left (L) and right (R) arms. Each task for each arm ...
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1answer
176 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
129 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
327 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
63 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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1answer
189 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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79 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
156 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. ...
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137 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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132 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
492 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
428 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
75 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
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 ...
2
votes
0answers
159 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
votes
1answer
614 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
votes
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: ...
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0answers
85 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
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 ...
5
votes
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), ...