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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1answer
363 views

Training and testing on Unbalanced Data Set

My data has 13,000 rows, 7% belong to the minority class. I used SMOTE (Synthetic Minority Oversampling TEchnique) for class balancing such that I raised the ratio of minority class to 42 % and number ...
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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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1answer
142 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
203 views

dealing with imbalanced data set in multiclass text classification

I need to build a text classification model. I have a labeled training set and my goal is to classify the new unlabeled text . My training set is composed on 6 categories, that are imbalanced. The ...
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0answers
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
2k views

Which post-hoc is more valid for multiple comparison of an unbalanced lmer-model: lsm or mcp?

After doing a model comparison with my mixed lmer model, I have a model with three main effects, no interaction, say ...
3
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0answers
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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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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2answers
2k 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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3answers
306 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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0answers
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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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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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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0answers
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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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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2answers
218 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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3answers
580 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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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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4answers
5k 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
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 ...
18
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2answers
3k views

Optimising for Precision-Recall curves under class imbalance

I have a classification task where I have a number of predictors (one of which is the most informative), and I am using the MARS model to construct my classifier (I am interested in any simple model, ...
3
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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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2answers
75 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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0answers
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 ...
1
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0answers
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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2answers
129 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 ...