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 add hard negatives to original training data?

I have 2 class binary classification problem with original training data of size N=n_pos+n_neg in general case n_pos!=n_neg but now we can assume that number of positive and negative examples near the ...
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8 views

How to set sum contrasts for unbalanced factors

Let's say that I have a model where the response time depends on accuracy (0/1, coded either as categorical or numerical) and another categorical variable (pres: idem/diff), both interacting with the ...
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23 views

Caret classification: feature selection & unbalanced data

I have a two-class classification problem with very unbalanced data (~1:1000 Yes/No ratio). The initial model class I'd like to try is regular glm. So there are two issues need to be addressed: 1) ...
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13 views

What is the method to find difference in mean of test and control population where data were collected for a marketing campaign?

What is the method to find difference in the response rate of test and control populations in SAS where data were collected for a marketing campaign? (10-20% were control, and the rest were the test ...
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6 views

How to do posthoc comparisons for unbalanced 2-way ANOVA (type II SS)?

I am using the car package to perform a type II ANOVA on unbalanced data. My two factors are "storm size" and "storm frequency." I have two storm sizes and four storm frequencies. I only have both ...
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11 views

How to create stratified subsets of one file?

I have one large file with class imbalance problem. I would like to stratify the subset into 10 subsets, and to preserve the ration of class sizes for each fold. So for example the overall class ...
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1answer
19 views

Balancing classes for Neural Network training

In a speaker recognition problem I have 330 speakers (classes) as targets and want to predict the identities with a feedforward neural net with a softmax output layer. The thing is some classes have ...
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10 views

Using priors, weights or costs for mitigating class imbalance?

A plethora of Matlab classifiers (e.g. tree-based or svm) allow to set priors, costs or weights for the data points. This can help dealing with imbalanced data. Unfortunately, none does support ...
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21 views

Tuning priors/weights/costs to counteract class imbalance

I have a classification problem which consists of two classes. It has high class imbalance. There are around 85% data points for the negative class and only 15% for the positive class. One option is ...
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10 views

Group treatment with unbalanced repeated measurments

In this study I want to determine if treatment group b and/or c are different from control group a. There are 13 individuals in the study. The groups are unbalanced as there is a different number of ...
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20 views

Is this a 2 fixed unbalanced ANOVA? How can be tested normality and homoscedasticity?

I need to know if my biological experiments show discrepancies between the condition used. In my experiments I have 2 fixed conditions: type of substrate (2 types) and chemical added (1 control + 3 ...
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10 views

Class imbalance and standard errors

I'm building a logistic regression that models the probability of conversion when clicking on a website ad. I'm not that interested in building a great classifier, but I want to identify a set of the ...
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1answer
31 views

When is dataset considered unbalanced?

I have data set which is highly unbalanced - target attribute is 93% False and 7% True. But I know that this is normal for my kind of data. I am afraid that if I undertake any steps (I can take less ...
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1answer
26 views

Random Forests with modified partitioning criteria

Here is the context of my question : I'm doing binary classification with unbalanced classes. The measure of performance I'd like to maximise is a modified F-measure : $$ F_{\alpha} = ...
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49 views

multiclass unbalanced data

I am trying to predict crimes (san francisco) using machine learning algorithms. Its a multi class classification problem with unbalanced data. I took sample of data ranging from years 2010 to 2015 ...
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19 views

Unbalanced two-factor repeated measures ANOVA with missing values

For my data set, I need to perform some sort of two factor repeated measures ANOVA. I have one between-subject factor called "Treatment" and one within-subject factor called "Frequency" with 8 levels. ...
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1answer
59 views

High Recall - Low Precision for unbalanced dataset

I’m currently encountering some problems analysing a tweet dataset with support vector machines. The problem is that I have an unbalanced binary class training set (5:2); which is expected to be ...
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1answer
33 views

Model Decay of Random Forest, when does it require an update?

I have built a random forest model on a dataset with a large class imbalance, I have attempted to maximize area under the curve when predicting on the test set. I wish to make a suggestion on when the ...
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21 views

Unbalanced three-class classification problem

I have three classes which are pretty unbalanced: A, B, and C with 3343, 135 and 1219 observation each respectively. Classes A and C are linearly separable (with ~96% accuracy), while the class B ...
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24 views

How to handle with “in class” imbalance in machine learning?

A lot is written about class imbalance in machine learning (for example on this site here). However, how to deal with "intra class" imbalance? Assume I want to classify Bikes v.s. Cars. My ...
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24 views

What techniques can I use to perform feature selection in the context of classification with an highly unbalanced dataset ?

I'm dealing with CTR prediction, which is a classification problem with an highly unbalanced dataset (around 1 positive class for 200 negative class). Most of my features (>90%) are categorical. ...
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1answer
80 views

Downsampling vs upsampling on the significance of the predictors in logistic regression

I've been trying to build a binary classification model using multivariate logistic regression using the caret package in R. My dataset consists of around 20000 observations from which >99% belongs to ...
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13 views

Interplay of Training Class Sizes, Class Weights, Loss function and Decision Threshold

I am facing a two-class classification problem where: There is way more training data in class 1 than in class 0. Classifying a class 0 event as class 1 has a higher loss than classifying a class 1 ...
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55 views

Is gradient boosting appropriate for data with low event rates like 1%?

I am trying gradient boosting on a dataset with event rate about 1% using Enterprise miner, but it is failing to produce any output. My question is, since it a decision tree based approach, is it even ...
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21 views

mixed effects modelling of unbalanced repeated measures data

I have radio tracking data on 34 animals over a period of up to 26 months. For about 6 animals I have all the data, for 2 others I only have a couple of months, and the rest lie somewhere in between. ...
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19 views

How implement sampling methods (for unbalanced data) in kfold cross-validaiton

Suppose that we have a unbalanced data-set for a binary classification problem and we want use 10-fold cross validation for training and testing fitted model. Is this correct that we only use ...
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49 views

Solve unbalanced data set problem in binary classification time series prediction (sampling methods)

I'm using time series data (continuous features) for binary time series prediction (one step ahead, up-turn and down-tern of output of t+1 comparing to ...
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1answer
38 views

output is a factor … how do I model it

If my input is numeric and my output is continuous I can use linear or nonlinear models. I can split the inputs by factors if an input is a factor. If my input is numeric and my output is boolean I ...
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26 views

use “spml” for unbalanced panel data?

I wonder if I can use R's "spml" package for unbalanced panel data. Millo's paper and example are all based on balanced panel data. I try to apply it to an unbalanced panel data set, but got the ...
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39 views

SMOTE algorithm how to select over and under percentage?

I have a highly unbalanced binary dependent variable (i.e. cases of '1' is <5%). I am trying to implement SMOTE algorithm using R DMwR package. I wonder in general, how we determine the parameters ...
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28 views

handle unbalanced data in multi-class

I have three classes A,B,C. They are different in their feature values. Another class D is the one I want to distinguish from A,B,C. From my perspective, I can treat A,B,C as one class (let's call it ...
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2answers
132 views

Cross validated penalized logistic regression - one standard deviation rule

I am new to this topic and would like to understand it better. I want to build a binary classifier based on penalized logistic regression. I have 10 features and 23 observations: 16 from class "0" and ...
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0answers
26 views

Minimize coefficient bias in regression with effects coded categorical variables where data is unbalanced and missing

I have a data set with two categorical variables that are effects coded. 6 out of 18 observations do not have records for the first categorical variable. 12 out of 18 observations do not have records ...
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1answer
28 views

When my response has a very skewed distribution, is it called unbalanced or imbalanced?

It is only a question of terminology. I am not a native speaker and was wondering, which term is used in what situation.
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43 views

What post hoc test should I run for a significant interaction in a two-way unbalanced ANOVA?

I have data with two factors (Category and Treatment) and each factor has two levels (A and ...
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20 views

Chi-squared test of independence for biased data

I'm working with a survey dataset consisting of 28807 observations (8470 males and 20337 females). I'm trying to determine the association between dichotomous variables, for instance, sex (Male, ...
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0answers
47 views

What loss function should one use to get a high precision or high recall binary classifier?

I'm trying to make a detector of objects that occur very rarely (in images), planning to use a CNN binary classifier applied in a sliding/resized window. I've constructed balanced 1:1 ...
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19 views

Classification with restrictions

I am working with multi-class classification. I have two sources of information for my classifier: I can get information only from the sample $x_i$. So my analyzer produces quite big number (~600) ...
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58 views

Precision in unbalanced multi-class problem

I am dealing with a multi-class classification problem and I compute micro-averaged evaluation metrics (precision, recall and F-measure) by performing 10-fold cross validation. However, the fact that ...
2
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0answers
130 views

Is using Rpart with unbalanced data a good idea?

I have a rather unbalanced data set and want to use rpart to build a classification tree. After building the full tree, I prune it back using the 1-SE rule. On average, only 1-2 splits are suggested. ...
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1answer
66 views

Is it valid to get better performance in logistic regression using only a subset of the coefficients?

I have an imbalanced data set containing 12% of the positive class 88% negative. First, I ran a logistic regression with all my coefficients and got an average accuracy of 0.91 (I know that's not ...
2
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1answer
110 views

Which cost function out of Logloss, AUC & overall error is better for unbalanced classes & why?

Why does Logloss & AUC perform better than overall error for unbalanced classes? How to choose between Logloss & AUC or unbalanced classes? FYI - I am referring to objective / cost function ...
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39 views

Class imbalance problem and baseline classifier

I have a dataset with four numerical attributes and a class (target) variable. There is an enormous imbalance between positive and negative instances according to class variable. To cope with ...
0
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1answer
31 views

Subset of training set produces good results while full training set produces poor results

I have an extremely unbalanced data set: around 200 positive samples and 70,000 negative samples. To overcome this problem I have tried to over-sample the minority class as suggested in previous ...
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1answer
18 views

How do you evaluate the performance of a classifier if its F1 is higher for one class but low for another?

For a binary classifier, how do I evaluate the performance if I'm getting very high precision & recall values (~0.9) for one class, say A, but lower (~0.5-0.6) values for the other class, say B? ...
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1answer
96 views

How to train classifier for unbalanced class distributions?

I attempted a ReLU neural network to classify data sets of 3 classes that are not balanced (in both training and test sets), i.e. 30% of samples are in class A, 10% in class B and 60% in class C. And ...
2
votes
2answers
246 views

Estimating classification probability, with low event rates — options other than logistic regression?

I am trying to predict the probability of occurrence of a low event rate outcome (~2% readmission risk after hospital discharge in the population of interest). With the available limited predictors, ...
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0answers
24 views

Why do I get nonmonotonic performance of linear SVM as I change binary class weight?

I have an unbalanced binary text classification task that I am trying to solve using Liblinear's [L2R_L2LOSS_SVC_DUAL][1] ...
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1answer
79 views

How to deal with unbalanced data and large dataset on low budget?

If we have a dataset with 5:1 Ratio and 500.000 observations we can randomly sample the majority class getting in this case 100.0000 minority class and 100.000 majority class? I'm wondering this ...
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92 views

Creating folds in cross validation

I have a question regarding cross validation. I have training data with response variables. Right now my code to split the data is: ...