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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How to deal with a skewed class in binary classification having many features?

I am doing data analysis in the mobile ad targeting domain. I have around 18 features and for a combination of these features, the result is either True or False (1/0) depending on whether the ...
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0answers
17 views

Should the number of normal samples always be more than that of anomalous samples in training set for anomaly detection?

I am trying to train an anomaly detection algorithm (one-class svm) on a data set with a few hundred positive samples and several thousands negative examples. Is it mandatory that I train the model ...
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10 views

Analysis over an unbalanced data set of independent variablees

I have a collection of 40 books publication of an author within different years(2005-2015). The number of publication is not even for different years. (the most is in 2014 and the least is for 2009) ...
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1answer
106 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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1answer
23 views

What is the best measure for unbalanced multi-class classification problem?

What are some possible classification metric for an unbalanced problem ? Due to skeweness of the distribution, accuracy value is not so meaningful. For instance, if I predict all the classes to class ...
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1answer
107 views

How to reduce number of false positives?

I'm trying to solve task called pedestrian detection and I train binary clasifer on two categories positives - people, negatives - background. I have dataset: number of positives= 3752 number of ...
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2answers
89 views

Classifer for unbalanced dataset?

Is there any classifer that can natively support unbalanced datasets? Or what best practices you can suggest to handle such datasets? For example I want to solve task called "pedestrian detection" ...
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1answer
44 views

SVM parameter tuning for unbalanced classes (with class weights)

I am trying to run an SVM on an imbalanced dataset (0-90%, 1-10%) using the e1071 package, with the radial kernel. I am using cross-validation to select the best gamma and cost. Additionally, I want ...
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2answers
625 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?
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1answer
81 views

How can I derive confidence intervals from the confusion matrix for a classifier?

I have am using k-fold cross validation to generate a confusion matrix for a classifier. I need to calculate 95% confidence intervals for the number of times each class is predicted when run against a ...
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1answer
116 views

Selection bias and reliability

I need a bit of help with interpretation of classification results. I have unbalanced data set (80% = 0 20% = 1), fitting classifiers (SVM, GradientBoosting or kNN) on such data does not yield good ...
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2answers
37 views

How is the chance-level confusion matrix calculated?

I applied an ML technique on my dataset, and got this confusion matrix: 0 1 0 162 62 1 27 50 Funnily, the overall accuracy is worse than ...
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1answer
39 views

What is the chance level accuracy in unbalanced classification problems?

Suppose one has a balanced classification problem (50% of 0's and 50% of 1's). In such a case, the so called chance-level accuracy of classifier would be 50%. What is the chance-level accuracy if the ...
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2answers
79 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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1answer
44 views

Help for interpreting SVM cross-validation results

I am using support vector machines for an unbalanced binary problem (0: 25%, 1: 75%). I do K-fold cross-validation with $K=10$. The metrics I get are: 80% classification accuracy on average for the ...
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0answers
7 views

Scaling before class rebalancing or after?

I'm training an SVM on my train data, which I'm then trying on my test data to find the accuracy of the algorithm. Would it normally be best to rebalance the class imbalance (2 classes with 60/40 ...
2
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1answer
48 views

12 firms and a total of 204 observations, can I use pooled OLS with firm-dummies or should I use fixed factor?

I am studying the effect of government ownership on firm performance, more specifically I am studying the effect of the government reducing their share in companies which are already partly ...
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3answers
2k 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 ...
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1answer
37 views

Effect of Misclassification Cost on SVM

I am using Matlab to train an SVM for very unbalanced data. However, my concern is not so much for the particular class assignment (ie 1/0), but rather to the scores (the prethreshold continuous SVM ...
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2answers
73 views

Bias-Variance tradeoff for classifying unbalanced classes

I would like to use Bias-Variance trade-off to evaluate training set size in a classification problem. There are two classes which are not balanced (~70/30) and it seems that the common use of ...
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0answers
39 views

Nested ANOVA with 3 random effects and unbalanced design

I would like to run a nested ANOVA to test three random effects (secteur, loc nested in secteur, site nested in loc) on the variable A. The design is unbalanced so I used lmer instead of aov. However, ...
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1answer
39 views

problems in doing logistic regression with unbalanced sample, give me some references

I have a dataset with lots Y=0 and few Y=1. I have to run logistic regression, so I'm using a retrospective sample in order to get a more balanced sample. Could someone give me some references that ...
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1answer
35 views

Skewed Classification Problem

So I've read around and seen this is a problem. I have a classification problem and 12 variables ... I'm working on getting more, but even if l get the number to 20-30 I feel like the problem will ...
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1answer
1k 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 ...
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35 views

Bias Correction for Large Scale Logistic Regression with Rare Events

I have a large dataset constituted of many ad impressions. My dependent binary variable clicked describe whether or not the ad was clicked on. As you can expect, the number of clicks is about 1000x ...
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1answer
35 views

Extreme unbalanced design: Group A: 43, Group B: 15,000

I've been asked to review a study comparing two groups, in which Group A=43 subjects and Group B=15,000 subjects. Intuitively, I feel that this is not a valid design, but can't find any specific ...
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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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132 views

Finding occurrences of specific patterns in time series

I have to locate occurrences of Cyllinder, Bell and Funnel patterns in univariate time series $X$ of gamma-ray sensoring. This is a specific case of the general CBF synthetic problem found in a few ...
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31 views

Classification Algorithm For Small Sample Sizes

I am looking at a problem now where I need to train a classification algorithm. There are only 2 classes, lets call them A and B, and I want a value between zero and one indicating the probability ...
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0answers
68 views

Optimize Probability Thresholds for class imbalances in glmnet models in caret

In direct relation to the topic discussed here I intend to retrain the model in order to optimize the probability threshold for classification of both classes. Currently the model achieves high ...
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1answer
76 views

Choosing a good binary classifier to be trained by a small set of labeled data

I have a small set of labeled data (diagnosis in individual subjects): ~50 of "sick" observations ~100 of "healthy" observations In reality, only ~1% of the observations are expected to be ...
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1answer
48 views

What does it actually mean for classes to be balanced?

I saw the following statement when reading Kuhn's APM: "The classes are fairly balanced; there are 111 samples in the first class and 97 in the second..." I thought balance would require the ...
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35 views

unbalanced groups in mixed design ANOVA

I want to perform a mixed design ANOVA. Time is the within subjects factor and the between subjects factor is Borderline, which is a categorical variable (borderline yes or no). There are 30 people ...
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24 views

Unbalanced groups and classification errors

I would like to adopt a general strategy for dealing with an very unbalanced dataset, where my "positive" group corresponds to 1/40 of all the observations. The reason why I ask it is because all the ...
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13 views

Non idependence within groups

I have to train a machine learning model for classifying two groups. Unfortunately, my positive group has a small number and many cases are not independent from each other (observations taken in ...
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3answers
665 views

Class imbalance in Supervised Machine Learning

This is a question in general, not specific to any method or data set. How do we deal with a class imbalance problem in Supervised Machine learning where the number of 0 is around 90% and number of 1 ...
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2answers
94 views

Balanced datasets in Naive Bayes

In a classification model, a desirable situation is to have classification classes evenly represented in the training dataset. Datasets that satisfy this property are called balanced datasets. ...
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0answers
40 views

Non-parametric Levene's test by Nordstokke and Zumbo

The example they mention is using a one-way ANOVA. What if I have two factors (3x11) and a dependent variable, can I do a two-way ANOVA to calculate the univariate levene's test? If so, how would I ...
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0answers
111 views

Best machine learning methtod for classificating datasets with non-independent cases within the groups

I have to perform binary classification of my data with supervised machine learning, but I have some difficulties working with my data set. It consists many genetic mutations that have parameters ...
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1answer
124 views

ROC curves for unbalanced datasets

Consider an input matrix $X$ and a binary output $y$. A common way to measure the performance of a classifier is to use ROC curves. In a ROC plot the diagonal is the result that would be obtained ...
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0answers
10 views

P-values for random effects when using REML [duplicate]

I'm using JMP to fit a model for an unbalanced split-plot design. Because it's unbalanced, I'm using REML rather than EMS. However, I would like to get test statistics/p-values for some of the random ...
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2answers
92 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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2answers
64 views

Class Imbalance

What are the best practices for fitting a binomial classification model when the classes are very imbalanced? For example, 99.9% 1's and 0.1% 0's.
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1answer
42 views

How to judge a partition is balanced or unbalanced?

Suppose we distributed $100$ coins to $10$ persons and the $i$-th person got ${x}_{i}$ coins, how to judge the distribution $X=\{{x}_{1}, {x}_{2}, ..., {x}_{n}\}$ (e.g., $X=\{5, 20, 15, 5, 10, 10, 10, ...
4
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1answer
69 views

Is up- or down-sampling imbalanced data actually that effective? Why?

I frequently hear up- or down-sampling of data discussed as a way of dealing with classification of imbalanced data. I understand that this could be useful if you're working with a binary (as opposed ...
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0answers
35 views

positive and negative sample count for ConvNets

I have been trying to set up a ConvNet to classify some data. This data should be classified to either 1 (being what I need to get from the image) and 0 for everything that is irrelevant. I have ...
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1answer
116 views

multiclass classification and unbalanced dataset

I have a five-class SVM multiclass problem. The dataset is small (about 160 examples) and unbalanced i.e. I have classes with few examples. So far I further limited the dataset to 110 examples in ...
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1answer
117 views

Permutation on multiple Pairwise Comparison with least square means in R 3.1.2

I'm working on a study where I employed an analysis of covariance (Ancova) with unbalanced factors. I used permutation tests to obtain p-values for my estimates since my observation does not come from ...
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0answers
10 views

comparing probability of imbalance classes

I am trying to figure out what id the standard way to compare the probability of occurrence of two imbalances classes: let say there 1500 web pages with different languages edition (e.g. Wikipedia). ...