Questions tagged [multiclass-classification]

In machine learning, multiclass or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification).

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What are some of the commonly used image processing techniques of OpenCV for multiclass image classification?

I'm working on multiclass skin disease image classification(caused by bacteria and fungus). Some of the sample images are shown below. Images contain different background as shown in image_1 and ...
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1D Sequence Classification with self-supervised learning

I am working on a multi-class classification task on long one-dimensional sequences. The sequence length may vary in the range $[512, 30720]$, and there is one feature associated each time-step in the ...
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Multiclass Ensemble Methods with weak classifiers under 50%

Normally, when using an ensemble method, such as baggin or boosting, in binary classification, there is a reqauirment that each weak classifier have accuracy better than 50%. In the multiclass ...
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Low accuracy and high loss in multi-class classification [closed]

I'm trying to classify images in 17 flowers dataset which consist of 1360 images of 17 classes (80 images per class); I have to use DNNs only therefore I made my model with the following settings: <...
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Is the average accuracy of each class (computed from the confusion matrix) equal to the accuracy calculated from cross-validation?

When I calculate the accuracy using cross-validation, it gives me a different accuracy than when I calculate using the confusion matrix. Why does it give a different accuracy? Is the accuracy ...
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Use soft-max post-training for a ReLU trained network?

For a project, I've trained multiple networks for multiclass classification all ending with a ReLU activation at the output. Now the output logits are not probabilities. Is it valid to get the ...
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How to label unsupervised data for deep learning multi-classification

I have unlabeled credit card transaction data that has the following columns: ...
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Multi-class classification but a single feature sometimes boils it down to a binary-classification

I have a three-class classification problem for a large dataset. Classes are 0, 1, and 2. There's a categorical variable in my feature vectors such that when a sample point has this variable positive, ...
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Connection between multi-label classification and multi-class classification

For a dataset with multi-label judgment, e.g., coco dataset but where we only want to predict the most possible label. There're multiple ways: train as multi-label learning and predict as a multi-...
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When to use Multi-class CNN vs. one-class CNN

I'm building an object detection model with convolutional neural networks (CNN) and I started to wonder when should one use either multi-class CNN or a single-class CNN. That is, if I'm making e.g. a ...
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How do I select the class weights for the loss function in the case of more than 2 classes?

I have a machine learning task where I would like to weight losses based on the frequency of the categorical values appearing in the data. The binary solution can be seen below, but I'd like to know ...
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Why would the "improvement" be the result of random initialization, and so why should we use multiple runs?

I got this feedback for my thesis paper. The improvement shown in the results section could be the result of random initialization. There should be multiple runs with means and standard deviations. ...
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How to deal with images that do not contain any object of interest?

I'm currently working on an iOS App where I want to detect if there is a table, chair or bench in the current camera input. My idea was to take the MobileNetV2 model and get it to classify these three ...
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What is the difference (if any) between semantic segmentation and multi-class, mutually exclusive classification?

Multi-class classification is simply assigning all data points into one of up to any finite number of mutually exclusive labels. I am new to the field(s) of AI/ML and I keep hearing people use the ...
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How to perform multi-class text classification with a dataset of 80 documents?

I have a training dataset of 80 text documents with an average number of characters in each document of 25000 and 210 unique tags. How can I perform multi-class text classification with such a small ...
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Why do we use the softmax instead of no activation function?

Why do we use the softmax activation function on the last layer? Suppose $i$ is the index that has the highest value (in the case when we don't use softmax at all). If we use softmax and take $i$th ...
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How do I calculate the probabilities of the BERT model prediction logits?

I might be getting this completely wrong, but please let me first try to explain what I need, and then what's wrong. I have a classification task. The training data has 50 different labels. The ...
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Using one-class classification first to find anomalies then apply multi-class classification

I'm new to machine learning and trying to apply it for fault detection, an idea came to mind which is using only anomaly detection after which if the results after a while come up as positive, a multi-...
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Multi class text classification when having only one sample for classes

I have a dataset of texts, each text was identified with an ID number. I would like to do a prediction by finding the best match ID number for upcoming new texts. To use multi text classification, I ...
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When computing the ROC-AUC score for multi-class classification problems, when should we use One-vs-Rest and One-vs-One?

The sklearn's documentation of the method roc_auc_score states that the parameter multi_class can take the value ...
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Is Mask R-CNN suited to solve a multi-class classification problem where the classes are related?

I want to create a model to solve a multi-class classification problem. Here are more details about my problem. Every picture contains only one object The background is very simple All objects ...
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Is it possible to combine k-fold cross-validation and oversampling for a multi-class text classification task with imbalanced data?

I am dealing with an intent classification task on an Italian customer service data set. I've more or less 1.5k sentences and 29 classes (imbalanced). According to the literature, a good choice is to ...
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How can I prevent the CNN from classifying a new input into one of the existing labels (it was trained with) when the input has a new different label? [duplicate]

I'm trying to perform image classification with a CNN. In my case, the inputs are the covers of 9 books, so there are 9 labels. I am using TensorFlow's Keras. If I pass a new input (that has a label ...
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What is the general procedure to use and train neural networks for multi-class classification?

I am very new to machine learning. I am following the course offered by Andrew Ng. I am very confused about how we train our neural network for multi-class classification. Let's say we have $K$ ...
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How to implement an "unknown" class in multi-class classification with neural networks?

For example, I need to detect classes for MNIST data. But I want to have not 10 classes for digits, but also I want to have 11th class "not a digit", so that any letter, any other type of ...
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