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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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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15 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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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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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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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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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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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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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, ...
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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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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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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 ...
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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. ...
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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
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 ...
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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
111 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] ...
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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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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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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 ...
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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 ...
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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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1answer
179 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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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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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 ...
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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
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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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
151 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
387 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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61 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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15 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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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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102 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
189 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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3answers
307 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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84 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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102 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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27 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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24 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
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 ...
3
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1answer
632 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
357 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
293 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
72 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
188 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
100 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 ...