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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For classification w unbalanced datasets, is class-weighing the same as oversampling?

in unbalanced classification problems, I find myself using class_weigh = "auto" or similar parameters often, but I don't think I'm fully understanding what it's doing. I know that it's the industry ...
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3answers
378 views

Is it right to build a logistic model for population with 2% of yes and 98% no population with 800k obs and 200 variables

I have a dataset which has has some 800,000 observations data at member level with some 200 features and it has a response flag of 1/0. The proportion of response 1 flag is 2% of entire member ...
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16 views

Need advice on unbalanced time-series dataset, for use with CAPM regression

I have 40 years of monthly historical returns of around 3000 mutual funds. The dataset contains both active and inactive funds, so some funds have data for the whole period, whereas others will have ...
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1answer
80 views

What's the measure to assess the binary classification accuracy for imbalanced data?

Now I have binary classification problem with positive samples roughly 100 times the number of negative samples. In this case the normal accuracy measure (predict == label) is not a good measure. What ...
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1answer
102 views

Class weights in caret

I'm using the R package caret to generate classifiers using a variety of different models on an imbalanced dataset. To overcome the class imbalance problem, I am using the "weights" parameter in the ...
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16 views

softmax in nnet with cost function

I have 40 classes in a classification problem. I'm using nnet with softmax but since the classes are very imbalanced I get the same probabilities for every case to predict. I read about F1 score. Is ...
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9 views

(logistic regression with imbalanced data) Does high polynomial degree in combination with rebalancing negatively affect accuracy?

Data Set: https://www.kaggle.com/c/GiveMeSomeCredit/data (cs-training.csv) Training Tool: Weka Data Processing Tool: Python (for higher polynomial degree) Question: Balancing data (by down sampling ...
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40 views

Sampling, feature selection and preprocessing in cross validation

To brief my question, I want to clarify the order of parameter tuning and the correctness of the flow in my scheme. In my classification scheme, there are several steps including: SMOTE (Synthetic ...
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2answers
77 views

what is the difference between area under roc and weighted area under roc?

Thanks in advance for the help. I have an unbalanced dataset that I am using for a binary classification problem. The classes are unbalanced. I believe that in such a case that weighted area under ...
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1answer
102 views

What balancing method can I apply to a imbalanced data set?

I'm trying to solve one classification problem from the UCI database repository. Unfortunately (or fortunately), I've noticed that my dataset is imbalanced. I've structured the data as 5 classes, ...
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17 views

What type of curve to use in object detection task to measure performance of detector?

I have an object detection task with one object type(one object type + background = object detection with sliding window as binary classification of each window). And my data is unbalanced(many ...
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58 views

Balancing random forest via cross validation. Difference between sample weight and cutoffs?

My random forest model of a simple binary target (0, 1) and is producing unbalanced results. i.e many more false positives than there are false negatives. In addition, '1' is a low percentage class, ...
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14 views

Model variables don't match stratification variables

Okay, so here's a puzzle that I recently encountered. Suppose I am interested in modeling a treatment effect (measured as a continuous variable) on a population, stratifying on a dichotomous ...
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23 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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1answer
71 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
228 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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1answer
127 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
154 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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2answers
58 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
96 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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1answer
68 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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14 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 ...
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1answer
64 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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2answers
85 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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86 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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61 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
53 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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55 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
47 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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40 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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115 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
126 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
55 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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1answer
136 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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42 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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15 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 ...
2
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0answers
36 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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2answers
157 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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1answer
125 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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119 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
252 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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79 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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3answers
1k 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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0answers
18 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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1answer
51 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, ...
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2answers
80 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
96 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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52 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 ...
0
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1answer
59 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
143 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. ...