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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609 views

Mixed-effects model for a strongly unbalanced design

I am somehow unsure on the best option to analyze these data. Here is my study case: The response variable is a morphometric measure, one for each individual. During 10 years, say 2000-2009, people ...
4
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0answers
382 views

Softmax regression bias and prior probabilities for unequal classes

I'm using Softmax regression for a multi-class classification problem. I don't have equal prior probabilities for each of the classes. I know from Logistic Regression (softmax regression with 2 ...
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 ...
3
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0answers
251 views

Mixed ANOVA: small and unbalanced samples

I have to analyze two samples ($n_1=10, n_2=18$) in a design in which there is a between-subject factor (Groups: 2 levels) and a within-subjects factor (...
3
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0answers
523 views

Problem with classifier after using SMOTE to balance the data

We've ran into a problem while training a classifier on an unbalanced data set. The response is binary with 0 indicating 'non defaulter' and 1 indicating 'defaulter' (it's a credit scoring task). ...
3
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0answers
385 views

How to use k nearest neighbours for binary classification with unbalanced classes?

I have relatively large (100k items) dataset which I need to split in two groups. So far I've tried knn and the results are not good mainly because I have disproportion in my training data: 90% of ...
2
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0answers
75 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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0answers
45 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 ...
2
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0answers
315 views

Average of predicted values with logistic regression

I have a large unbalanced dataset (the target has ~1500x more 0's than 1's) on which I train a logistic regression algorithm to predict the probability of success (Not a binary outcome but a real ...
2
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0answers
217 views

Unbalanced panel data: Fixed effects?

I have an unbalanced panel dataset with N=10 firms and T=61 days. Because one variable had values outside the theoretical range I had to constrain my dataset, which left me with only 239 observations. ...
2
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0answers
441 views

Repeated measures ANOVA for an experiment with missing values

I have an experiment where several subjects (subjects $= S_1,S_2,...,S_m $) were asked to perform a set of tasks (tasks $= T_1, T_2, T_3,...,T_n$) using both their left ($L$) and right ($R$) arms. ...
2
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652 views

How to analyze an unbalanced within-subject ANOVA design?

I have data from an experiment involving 4 groups of subjects, 2 possible interventions first and 3 possible intervention at a 2nd point, repeated data measurements from each subjects multiple time ...
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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 ...
1
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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, ...
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0answers
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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0answers
52 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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0answers
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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0answers
107 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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0answers
71 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 ...
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20 views

Multiclass target detection : N X (1 vs all) or 1 X (N vs all) ?

I am doing a multiclass classification using neural networks. say I have 10 target classes and one null (non-of-the-above-targets). is it better that I train a neural network separately for each ...
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0answers
46 views

Achieving high recall for smaller class in unbalanced linear svm

I have an svm-related question. I have an unbalanced dataset, meaning classA could be 1/10 to 1/35 of classB. Well I am interested in getting a linear svm which would separate the data and would ...
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0answers
141 views

How to do a power analysis for an unbalanced mixed effects ANOVA?

I need suggestions for how to calculate the $n$ required for 80% and 90% power for >30% change from baseline ($T_0$) at $T_1$ or $T_2$, drug vs. placebo, 2:1 ratio; 20% CV in test; factors: subject, ...
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41 views

Is it correct to compare two distributions of observed (that are means of observation) and expected values with chi square?

I have 2 classification methods that I want to compare. So I ran them on the same dataset and I obtained 2 different classifications. Then, I want to test if their classification is robust and how. ...
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99 views

Testing classifier with binomial test when group sizes are unequal

I have data from 50 human subjects, who are divided into groups A and B (30 participants are in group A and 20 participants in group B). I also have a range of measurements from each subject. I have ...
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245 views

Questionnaire analysis and grouping responses

I want to know if the creative process engagement (CPE) is related to creativity. I don't know how to analyze the questionnaire. I will be collecting data for CPE with Zhang and Bartal's (2010) ...
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92 views

Type I ANOVA tests not depending on the order of the factors

I have a dataset with two factors A and B and the following design (contigency table showing the number of individuals for each ...
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0answers
118 views

Different Mean Square partitions in an unbalanced bifactorial ANOVA (with random factor) between R and Statistica

I am trying to extract variance components for selection and chance in a bifactorial design with Generation as a fixed factor and Replicate as a random term, for early fecundity. Since I am using ...
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223 views

How to compare multiple groups of unblanced repeated measures non normal data?

I'm trying to compare three groups data. But the data set is about a new drug trial. The data set has these characteristics: Follow-up set. That is, after administration of the drug, a series of ...
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0answers
1 view

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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0answers
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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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 ...
0
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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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29 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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0answers
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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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 ...
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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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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 ...
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0answers
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 ...
0
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0answers
60 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 ...
0
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0answers
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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0answers
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 ...
0
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0answers
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 ...
0
votes
0answers
101 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, ...
0
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0answers
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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0answers
22 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 ...
0
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0answers
178 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, ...
0
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0answers
51 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 ...