Linked Questions

6
votes
2answers
1k views

Named entity recognition and class imbalance [duplicate]

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 ...
0
votes
1answer
6k views

How to undersample with algorithms in R to solve class imbalance? [duplicate]

My data set is imbalanced - 5% of the target class represents fraudulent transactions, 95% of the target class represents legitimate transactions. I must use the whole data set, as the 95% of ...
0
votes
1answer
2k views

How to do imbalanced classification in deep learning (tensorflow, RNN)? [duplicate]

I am trying to do binary classification of News Articles (Sports/Non-Sports) using recurrent neural net in tensorflow. The training data is highly skewed [Sports:Non-Sports::1:9]. I am using cross-...
0
votes
1answer
221 views

Unbalanced dataset accuracy [duplicate]

I’m currently encountering some problems analyzing a dataset with neural network. The problem is that I have an unbalanced binary class training set (10:1). Training accuracy for both classes are 100%....
1
vote
1answer
57 views

Training binary classifiers with huge dataset with mostly negative examples [duplicate]

I would like to build an ensemble classifier (possibly boosting) on a huge training dataset (>> 1e7 examples) where the proportion of positive examples is around 5%. And what I am interested in are ...
0
votes
0answers
36 views

Training set target categories' distribution [duplicate]

In a book I'm reading I've come across the following quote: Accuracy on the test set is a good performance measure only when there is a relatively uniform distribution of target categories in the ...
44
votes
5answers
8k views

When is unbalanced data really a problem in Machine Learning?

We already had multiple questions about unbalanced data when using logistic regression, SVM, decision trees, bagging and a number of other similar questions, what makes it a very popular topic! ...
4
votes
4answers
6k 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 ...
2
votes
2answers
2k views

Classification algorithms for handling Imbalanced data sets

I’m working on a classification problem where dataset is extremely imbalanced ( roughly 13000 "zero" and 100 "one" responses). As the first step, I trained a Logistic Regression and changing the ...
2
votes
2answers
615 views

Balance classes when sampling

I have a large dataset describing numerous customers' behaviour and I am trying to solve a binary classification problem with a null accuracy on 90% (90/10 distribution amongst the two classes). ...
-2
votes
2answers
409 views

Training a 3 million sample data which has unbalanced labels

I have data which has 3 million samples and unbalanced label. I have tried many neural network approaches, but I couldn't get a good result. Which path do you suggest me to follow in this case, in ...
3
votes
0answers
1k views

Classification on highly skewed dataset

I have two classes A and B. 98% of the data belongs to class A and 2% of it belongs to class B. Size of the entire dataset is about 2000. I am interested in correctly classifying all the data points ...
1
vote
2answers
729 views

Predictive Decision Tree in R

I am currently working on a dataset in R-studio and as the title might suggest I am having difficulty creating the tree I'm looking for. My dataset consist of 122151 observations with 33 Variables. ...
1
vote
1answer
303 views

How would you represent this one-vs-all SVM accuracy?

I have a set on one-vs-all SVMs. Let's say I have three classes. I want to show FAR and FRR from the system, but I appear to get getting very large FRR values and very little FAR values. This is ...
2
votes
1answer
209 views

class imbalance problem in machine learning

For a training data set (60 positive class samples and 40 negative class samples) of SVM learning algorithm. Are the two oversampling methods the same? (1) bootstrapping 40 negative samples into 60. ...

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