Questions tagged [semi-supervised-learning]

Making use of both unsupervised and supervised learning paradigms to train on a partially labelled dataset.

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26 views

Is it better to have one model with more categories or less with two for multi-label classification?

For classifying text into three classes question, complain and complements where each sample can have multi-labels (question and complains, question and complements): is it better to have one model ...
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1answer
38 views

How to approach semi-supervised binary classification problem with few labels only from one class?

I confront with a binary classification problem where I do have a few instances with labels (so far this is "semi-supervised" learning as far as I know), but only from the positive class. So ...
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1answer
27 views

Identifying templates from SMS text

I am building an app where I identify information from the SMS, something similar to expense management apps. I have a parser which reads all the SMS of user, identifies SMS of interest and parses ...
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0answers
17 views

How do I build a model to improve CTR on campaign?

I am trying to build a propensity model for a client to increase the CTR. Client has the list of people who clicked in the previous campaigns but doesn't have the data on the list of people who didn't ...
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1answer
39 views

Semi-supervised anomaly detection

I am currently exploring anomaly detection methods for my work and, basically I have gone through Local Oulier Factor and Isolation Forests, both unsupervised methods. Now, the thing is, there might ...
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1answer
27 views

What is the principle of Unsupervised Data Augmentation (UDA)? Why does UDA work?

UDA(https://github.com/google-research/uda) could achieve good accuracy by only 20 training data on text classification. But I find it is hard to reproduce the result on my own dataset. So I want to ...
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32 views

What is a semi-supervised learning model capable of detecting new classes?

Suppose I initially want to distinguish between dogs and cats based on various numeric features (tail length, weight, etc). I have some labeled data for both classes, but also a large amount of ...
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7 views

Data points are highly overlapped and do not follow smoothness rule assumption

I am working on a very high dimensional categorical features based data set. There are two output classes and 2-dimensional PCA plot suggests that the data points belonging to both +ve and -ve classes ...
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0answers
16 views

Transductive Multilabel Classification

I'm trying to use transductive (semi-supervised) multilabel classification on my dataset since I have a low volume of labelled data samples, compared to the unlabelled samples. I found a promising ...
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1answer
32 views

What is the difference between all the different types of learning within machine learning?

This is a question that is really hard to google, and the differences are confusing. Does anyone have good examples of the differences between them all? Supervised Learning Semi-Supervised Learning ...
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1answer
30 views

Validity of PU learning while using character-level encoding using CNNs for classifying text data

I'm trying to classify a large set of documents (~100M) as valid or invalid, based upon a small given set of labeled valid documents (~3k). I'd like to know if the PU learning approach described in ...
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11 views

Is it wise to include the target labels when a supervised learning problem is tackled as an unsupervised learning problem?

I have a problem which requires both a supervised and unsupervised learning approach. This is because I am trying to find some hidden clusters (if they even exist) beyond the labels of the dataset. ...
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4answers
68 views

Supervised clustering

I'm working on a clustering problem. I have a training set composed of sets of points where the clusters are known and I want to find the good clusters on a testing dataset. It's a kind of supervised ...
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262 views

Inductive vs Transductive Learning

I am reading about Inductive and Transductive Learning. Some of the questions that come to mind are the following: What is the difference between these two? Which algorithms are usually employed for ...
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0answers
29 views

using dataset to classifying and labelling another unlabeded dataset

I collect a collection of posts from Facebook and I use a published sentiment datset to labeling my collected dataset. is this a right technique and what its name is this transfer-learning ?
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1answer
39 views

Is there an algorithm for categorizing unlabeled samples into K classes? [closed]

I am not sure if this would be considered unsupervised, or semi-supervised learning. I am looking for an algorithm that will take N input arrays of features, and then cluster samples(not features) ...
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28 views

Ignoring unlabeled data for a single class

I have a data set of transactions with a binary flag labeling each as fraud or not fraud. However, it can take up to 90 days for a transaction to reveal itself as fraudulent. Sometimes it happens in a ...
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3answers
405 views

What kind of learning is needed for anomaly detection? Supervised learning, semi-supervised learning or unsupervised learning?

I am doing anomaly detection recently, one of the methods is using AEs model to learn the pattern of normal samples. Determine it as an abnormal sample if it doesn’t match the pattern of normal ...
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1answer
114 views

What is the convergence criteria of a semi-supervised learning algorithm?

I would like to know when to stop doing semi supervision? For example, if I learn a classifier from a small dataset and then use it to label a pool of unlabelled dataset. I then use the newly labelled ...
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1answer
33 views

Accuracy after selftraining didn't change

I used Decisiton Tree Classifier which I trained with 50 000 samples. I have also set with unlabeled samples, so I decided to use self training algorithm. Unlabeled set has 10 000 samples. I would ...
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1answer
431 views

How to get probability of classification

I have binary classification, I tried several model KNN, SVM, decision tree and random forest. I have 50 000 samples, X_train has 50 000 rows and 2300 columns. ...
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0answers
18 views

General way of constructing adjacency matrix in Laplacian SVM semi-supervised technique

I am trying to implement a Laplacian SVM classifier (trained in primal) using algorithm from this paper. I would like to know what is the most common way of constructing adjacency matrix and the most ...
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0answers
18 views

Neural Network for detecting/checking for requirements in diagrams

My question is more about what approach is a good/the best approach for my problem: THE PROBLEM - I'm an (mechanical/software) engineer and we take extensive amount of time to review technical ...
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0answers
54 views

Improving automated ingestion system using Machine Learning and/or NLP

I'm working on a automated ingestion system which takes a PDF or doc file or a URL. It then parses the file and get me the required text in a json format but there are some error and there are few ...
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1answer
31 views

Adapting Neural Network to new domain without labels

Is there an approach for the following problem: Lets say, I trained a neural network on a big dataset for categorizing different fruits in $k$ classes. Afterwards I got a nice model, which performs ...
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1answer
231 views

Semi-supervised learning for regression

It is mentioned on this page that Label Propagation of scikit-learn can be used for regression also. However, nothing is ...
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1answer
145 views

how to build a predictive model without training data neither historical data

I m trying to score "how much a product is expected in the market". I created some features: How much this product is used each year. Where was it used . how many product for each country. the main ...
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1answer
467 views

Semi Supervised Learning without label propagation

I am trying to cluster some words by affinity. Using Word2Vec I obtained vector representation of every word that I can cluster with a normal unsupervised method. Of these words, though, I know the ...
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0answers
82 views

Semi-supervised Learning doubt

I'm reading "Hands on machine learning" by Aurelien Geron. He stated that semi-supervised learning is: Some photo-hosting services, such as Google Photos, are good examples of this. Once you ...
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2answers
288 views

Time series binary classificaiton with labelling issues

My situation is quite complicated so I will give a similar example from a simpler domain. Suppose we want to try to predict WHEN a mobile game users will make a purchase if given a sale. Almost every ...
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1answer
424 views

suggest ingredients based on recipe title [closed]

I would like to construct system that would suggest user ingredients once he/she inputs title of the recipe. I think that this is the task of machine learning or AI, but on the other hand I am pretty ...
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1answer
34 views

Problem faced when collect data randomly from cluster [closed]

I have a semi structured data set. I need to collect some data (unlabeled) randomly for labeling. As initiative at first I separated labeled and unlabeled data. Then I convert those data from string ...
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1answer
630 views

How to do semi-supervised learning for regression

I am doing regression analysis on a data set with over 20000 samples using scikit learn. Trying to use regression models to fit three features to label which is a score ranges from 0 to 10. Problem is ...
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1answer
83 views

Can You Purposely Bias A Clustering Model?

We have a large amount (Billions) of high cardinality, mixed nominal & numerical data, and are performing some clustering on it as an experiment. There is a small subset of these data, however, ...
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1answer
9k views

Custom conditional loss function in Keras

I'm looking for a way to create a conditional loss function that looks like this: there is a vector of labels, say l (l has the same length as the input x), then for a given input (y_true, y_pred, l) ...
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4answers
6k views

Why positive-unlabeled learning?

Machine learning can be divided into several areas: supervised learning, unsupervised learning, semi-supervised learning, learning to rank, recommendation systems, etc, etc. One such area is PU ...
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1answer
407 views

How should I construct a binary classifier for thousands of positive data and millions of unlabeled data?

So far, I have stumbled upon many advices and papers on PU Learning and Unary classification. TLDR: Does anyone have suggestions for specific algorithm or implementation for labeled data of only one ...
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1answer
156 views

Probability for label correctness in semi-supervised learning

I am aware of the existence of semi-supervised learning approaches, such as the Ladder Network, where only a subset of the data is labeled. Are there any methods or papers which consider correctness ...
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2answers
648 views

General strategy for imbalanced, semi-supervised, sparse problem

I am looking for some general advice on where to start with this problem. There are 350 sparse (low positive integer) features. I have 2000 positives, 1000 negatives, and infinite unlabeled data, ...
5
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3answers
173 views

Predictive clustering

I have an hypothesis but i don't know if it's true. If the cluster is dense and we apply a supervised learning on this data, the model generated by this cluster will be more efficient for new data ...
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1answer
215 views

Generic strategy for object detection

I have a huge collection of objects from which only a tiny fraction are in a class of interest. The collection is initially unlabelled, but labels can be added using an expensive operation (for ...
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3answers
2k views

Build a binary classifier with only positive and unlabeled data

I have 2 datasets, one with positive instances of what I would like to detect, and one with unlabeled instances. What methods can I use ? As an example, suppose we want to understand detect spam ...