All Questions
Tagged with class-imbalancedeep-learning
40 questions
2votes
1answer
79views
Why do we need Smote?
We use Smote to balance the imbalanced dataset but why we are manipulating things and cannot use the natural data i mean what is the need for balancing what exact impact it will make to model
1vote
2answers
33views
What do these train and test accuracy and loss graphs suggest ? Can train and test accuracy reach 80% after one epoch?
This is the accuracy and loss plot for CNN model. Is it possible that train and test accuracy may starts from 80% from the 1st epoch itself for 5 k fold.
3votes
1answer
90views
When using class weights is bad?
I have a DB with 50 different classes. One of the classes has x10 more data than the other classes. Each class has ~20K samples and the 'big' class has ~200K samples When training classification model ...
- 689
0votes
0answers
30views
Using ResNet50 with SE block on imbalanced data - pytorch
I worked with a breast cancer ultrasound image dataset containing 432 benign cases, 210 malignant cases, and 133 normal cases. Initially, I used a pretrained ResNet-50 model, which yielded the ...
0votes
0answers
43views
How to handle imbalanced edge weights in a graph for node embedding and edge weight prediction?
I have an undirected weighted graph where the edge weights represent probabilities. The majority of the edge weights are 1 (which are 7 times more frequent than the second major group of weights). I'm ...
0votes
0answers
42views
Extremely Imbalanced and Gapped Dataset in Regression Problem
Currently I am working with a biological dataset with a range of 0-to-1 to do a multi-task regression with Deep Learning. However, this dataset has an empty gap in the range 0 to 0.2 (however there ...
1vote
0answers
70views
How to solve imbalanced dataset oversampling problem in multi labels-classes instance segmentation task?
I want to use models YOLOv7-seg for instance segmentation of tree species in images. There are 26 species of trees, and each image may contain multiple species. There is a distinction between dominant ...
- 11
0votes
1answer
204views
Difference between class_weight and loss_weights arguments in TensorFlow/Keras
I am creating a neural network using TensorFlow (v2.9.2) for an imbalanced image dataset. While doing so, I noticed that model.compile() method has an argument <...
0votes
1answer
158views
Classification on severe Class Imbalance high dimensional data
Dear DataScience Community, I am working on class imbalance tabular data with high-dimension inputs. The tabular data is derived from the satellite data pixels, and I have inflated the train data ...
1vote
1answer
4kviews
class weights formula for imbalanced dataset
I am trying to make some semantic segmentation. I have 7 imbalanced classes in my case. I found several methods for handling Class Imbalance in a dataset is to perform Undersampling for the Majority ...
- 11
2votes
1answer
556views
CNN unbalanced and small dataset
I would like to use CNN to make classification with 5 classes, but 4 of these classes only have between 16 and 60 images, while the last one has more than 1300. I know 16 or 60 images are not enough, ...
- 41
1vote
2answers
837views
Label distribution over training, validation and test [closed]
I am wondering over whether the number of classes distributed over my training, validation, and test label affects the model.
2votes
1answer
259views
Deep learning with Imbalanced classes [duplicate]
I am trying to model a packet data with 1 dimensional CNN but I have a very imbalanced classes in my target. I have 3 classes as class 0 has 53000 cases, class 1 has 300 cases and class 2 has 150 ...
-2votes
1answer
40views
How to achieve better accuracy of 90+ on a 3 class highly skewed dataset?
I have a 3 class dataset with very high imbalance classes: class 1: 75000 class 2: 27000 class 3: 3000 With simple learning algorithms, accuracy is ...
- 119
0votes
2answers
96views
Creating dataset - imbalanced or balanced?
I'm trying to make an image classification model and I have 5 classes - A, B, C, D, E. The goal is to get the highest possible classification accuracy. I have a database of images and I'm selecting ...
- 101
