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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27 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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3answers
312 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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3 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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1answer
133 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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0answers
16 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
3k 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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1answer
160 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
15 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
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, ...
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2answers
69 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
15 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
24 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
67 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
18 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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0answers
14 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, ...
0
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1answer
141 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 ...
2
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1answer
289 views

Training and testing on Unbalanced Data Set

I used SMOTE algorithm in R for class balancing. My data size has 13000 rows, I had 7% minority class in my sample now I used SMOTE( Synthetic Minority Oversampling Technique) for class balancing such ...
2
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2answers
235 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
30 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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0answers
18 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
113 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 ...
3
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0answers
48 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
76 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. ...
2
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2answers
902 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
62 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 ...
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1answer
65 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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34 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 ...
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1answer
180 views

R - Classification ctree {party} - Testing sample and leaf attribution with unbalanced data

Let's start with data description of the website visits I analyse : 6M rows Dependant variable quotation is binary and takes values ...
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1answer
24 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 ...
0
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1answer
11 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
76 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 ...
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2answers
72 views

How to choose sampling method for imbalanced data?

I have an imbalanced dataset with 4995:5 ratio as well as other datasets with less imbalanced ratios. I split this 4995:5 ratio into training and testing for about 2/3 training and 1/3 testing. I also ...
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3answers
5k 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: ...
2
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2answers
113 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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20 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] ...
0
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1answer
69 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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0answers
90 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: ...
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0answers
14 views

Implications of sampling complete data set prior to classification

I want to ask about the consequences or things to keep in mind when sampling the complete data set to overcome class-imbalance issue. I have come across numerous examples where only the training set ...
3
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1answer
72 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 ...
8
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3answers
3k 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 ...
1
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1answer
79 views

Multi-class classification with imbalanced classes

I have a data from 5 classes and I would like to build a classifier. However the number of feature vectors in each class is very different. One has about 5000, one about 200,000, one about ...
0
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0answers
30 views

Orthogonal contrasts in a logistic mixed-effects model with unbalanced dataset

I have a dataset containing a dichotomous outcome variable Y, and 3 independent variables: ...
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0answers
24 views

Class assignment across multiple categories

I currently have a dataset with four segments that were created off of survey data that we are trying to score records into one of the four segments based on another set of behavioral data. The ...
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0answers
88 views

SVM classification of unbalanced data with SMOTE sampling - overfitting?

DATA: 109 negative cases, 14 positive cases, 4 features. MODEL CHARACTERISTICS: SVM with RBF kernel and SMOTE sampling (Chawla N.V. et al., 2002). MODEL TUNING: Grid search of C and gamma. For each ...
3
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1answer
152 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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2answers
2k views

Does GBM classification suffer from imbalanced class sizes?

I'm dealing with a supervised binary classification issue. I'd like to use the GBM package to classify individuals as uninfected/infected. I have 15 times more uninfected than infected individuals. I ...
2
votes
3answers
397 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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0answers
53 views

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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26 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
389 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 ...