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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Choosing the best featureset for prediction

I have this N sets of features F each with $F_i$ number of features. All the feature sets have 20000 examples and we have 20,000 labels for them. Lets say feature set $F_1$ has 10 features and ...
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30 views

Class Imbalance

What are the best practices for fitting a binomial classification model when the classes are very imbalanced? For example, 99.9% 1's and 0.1% 0's.
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1answer
28 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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1answer
27 views

Is up- or down-sampling imbalanced data actually that effective? Why?

I frequently hear up- or down-sampling of data discussed as a way of dealing with classification of imbalanced data. I understand that this could be useful if you're working with a binary (as opposed ...
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2answers
49 views

Balanced datasets in Naive Bayes

In a classification model, it is well known that a desirable situation is that all possible classification classes are evenly represented in the training dataset. Datasets that satisfy this property ...
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0answers
9 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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1answer
14 views

Affect of Misclassification Cost on SVM

I am using Matlab to train an SVM for very unbalanced data. However, my concern is not so much for the particular class assignment (ie 1/0), but rather to the scores (the prethreshold continuous SVM ...
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1answer
87 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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1answer
21 views

multiclass classification and unbalanced dataset

I have a five-class SVM multiclass problem. The dataset is small (about 160 examples) and unbalanced i.e. I have classes with few examples. So far I further limited the dataset to 110 examples in ...
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1answer
940 views

Using AdaBoost on multi-class in R on unbalanced data

I have a data set which is highly imbalanced and I have used the SMOTE algorithm (using the R package DMwR) to balance the binary class in the data set. I have been using the R Ada package to then ...
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2answers
83 views

How to deal with a skewed class in binary classification having many features?

I am doing data analysis in the mobile ad targeting domain. I have around 18 features and for a combination of these features, the result is either True or False (1/0) depending on whether the ...
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1answer
22 views

Permutation on multiple Pairwise Comparison with least square means in R 3.1.2

I'm working on a study where I employed an analysis of covariance (Ancova) with unbalanced factors. I used permutation tests to obtain p-values for my estimates since my observation does not come from ...
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7 views

comparing probability of imbalance classes

I am trying to figure out what id the standard way to compare the probability of occurrence of two imbalances classes: let say there 1500 web pages with different languages edition (e.g. Wikipedia). ...
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0answers
28 views

cost matrix, unbalanced class, oversampling and threshold probability

Let's suppose I have a cost matrix with TP=+90 FP=-10 TN=0 and FN=-10, and that the class is unbalanced. I need to capture the costs in my decision. To do so, I always consider the probability ...
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1answer
26 views

Binary classification in imbalanced data

I have a binary classification problem where my data is highly imbalanced (80:20). Given this imbalance is present in both training and test set, does it make sense to apply specific strategies during ...
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0answers
30 views

SVM One-vs-One vs One-vs-ALL SVM

For an unbalanced dataset annotated by human annotators in which each item is assigned to different classes, what is the argument for and against using any of One-vs-One vs One-vs-ALL SVM ...
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1answer
76 views

Has anyone publicly shared an implementation of RUSBoost in R?

There's no package available on CRAN, so I was hoping someone in the community had written their own function/package. I see it's been done in MATLAB, so I may just have to start with that and write ...
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2answers
79 views

Does Support Vector Machine handle imbalanced Dataset?

Does SVM handles imbalanced dataset? Is that any parameters (like C, or misclassification cost) handling the imbalanced dataset?
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1answer
309 views

Imbalanced data classification using boosting algorithms

I am working on a binary data classification problem. The dataset is imbalanced, it consists of 92% 'false' labels and 8% 'true' labels. The number of features is 18 and I have a small number of 650 ...
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1answer
72 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 ...
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1answer
41 views

LSmeans - Unbalanced data with interactions

I wish to analyze an unbalanced data set with 3 variables Tleaf, Tair, and orientation (factor with two levels). Considering the effect of the factor "orientation", I wish to determine if "Tair" has a ...
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0answers
99 views

Any advice on how to improve my accuracy rate in text classification?

I'm trying to do a text classification task. Here are some specs: Context file size = 1M+ documents already labeled Number of top-labels = 17 Number of sub-labels = around 130 Each document is ...
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3answers
291 views

Suggestions for cost-sensitive learning in a highly imbalanced setting

I have a dataset with a few million rows and ~100 columns. I would like to detect about 1% of the examples in the dataset, which belong to a common class. I have a minimum precision constraint, but ...
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29 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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50 views

unary classification in PyBrain

I've just started using PyBrain for some data classification work, and I've gotten it working pretty well where I have data from all possible classes and I can train the network using all the classes. ...
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0answers
28 views

Unbalanced groups in a Hierarchical Linear Model

I would appreciate some practical and/or conceptual advice on sample sizes in a two level hierarchical linear model. A lot of the material on sample sizes and HLM is about the number of level 2 ...
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9 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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2answers
55 views

If one group has 23 participants and the second group has 117 participants in it, can we rely on the result of t-test? [duplicate]

I was evaluating a research paper in which the authors have 23 male participants and 117 female participants. They have applied t-test to calculate Gender differences and have even concluded on ...
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3answers
77 views

Significant main effect in one-way, but not in two-way, MANOVA

I used a one-way MANOVA to study the effect of age groups on the average of height and weight and found that they were significant. Then I used a two-way MANOVA to study the effect of age groups and ...
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3answers
584 views

Correlation and group difference with unequal sample sizes

My sample is 60 participants. At first, I did not expect to analyze the correlation, but the result is interesting, so I would like to. The 60 participants are 50 people who have good reading skill ...
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2answers
894 views

Dataset of repeated measures with unbalanced sample size and unequal variace - What to do?

I have a dataset of three groups of cells treated with 10 different compounds and am not sure how to check for significant differences between those treatments. Within each group the data is also ...
2
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0answers
80 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 ...
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2answers
291 views

Named entity recognition and class imbalance

I have implemented Maximum-entropy Markov model (MEMM) for the Named entity recognition (NER) problem. I have four classes: geographical, people, material (book titles etc) and other. Class ...
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0answers
88 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. ...
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2answers
442 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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0answers
18 views

How to analyze data with unequal observations per cell?

I've got a very poorly designed study, and I need to find a way to analyze the data I've collected. Here's a description of the design: There are two within-subjects variables, one with four levels ...
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2answers
106 views

Which classifiers work well with unbalanced data?

I have a binary classification problem which is very unbalanced - it can have 98% of data from one class. Which classifiers work well with this sort of data? I have an unlimited supply of training ...
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21 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
43 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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1answer
51 views

Unbalanced test data matter?

I have balanced training data: 300 positives and 300 negatives, but the test data is unbalanced: I have 15 positives and 60 negatives. Will the unbalanced test data impact classification accuracy? ...
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1answer
94 views

Logistic sample and case numbers

I have some questions about binary logistic regression. For my research, I am planning to use 12 predictors, and my sample consists of 129 cases. However, I know of a 1 to 10 rule. Additionally, my ...
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0answers
102 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 ...
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2answers
176 views

How to deal with unbalanced data

I'm doing data analysis with a dataset of 11795 data points (with 88 features). 85% (9973 points) of these data points correspond to data points belonging to class 1, 5% (589 points) belong to class 2 ...
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0answers
21 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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60 views

Oversampling with categorical variables

I would like to perform a combination of oversampling and undersampling in order to balance my dataset with roughly 4000 customers divided into two groups, where one of the groups have a proportion of ...
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0answers
19 views

the effect of unbalanced classes

I have a data set with label "Y" and "N", which is splitted into training and testing sets. The ratio the labels in training and testing sets are 3:1 and 1:1, respectively. If I build model using the ...
0
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1answer
69 views

Improving SVM classification

I have a classification problem (bioinformatics domain) where I have around 333 features. Currently, I am first selecting features (using importance feature of random forest) and then pushing the same ...
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0answers
121 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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0answers
25 views

How to account for multiple & varying amounts of observations per factor level & still retain info? As a random effect in GLMM or take the mean?

I would appreciate any help! Specifically I would like to know which option is best. Question Does Var2 influence Var1 in relation to the factor(Ind), and does Var3 & Var4 also have some effect. ...