Questions tagged [classification]

Statistical classification is the problem of identifying the sub-population to which new observations belong, where the identity of the sub-population is unknown, on the basis of a training set of data containing observations whose sub-population is known. Therefore these classifications will show a variable behavior which can be studied by statistics.

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Stop Word removal with RNNs

Is it required or encouraged to remove stop words from documents when using Recurrent Neural Networks for Text Classification? To my understanding RNNs are able to "understand" words in a context and ...
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Can I use HMM to predict the spread of Ebola?

1) Can Hidden Markov Model be used across both a large number of categories (districts) and cases (weeks)? 2) Is HMM appropriate for trying to model such a problem? 3) Would I need to develop a ...
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How do I make use of “soft” labels in binary classification?

Let's say we have a binary classification task, but our dataset contains more fine grained values of how much an examples belongs to the class or not. So the labels are real numbers in $\left[0,1\...
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Training instances importance in Random Forest?

Is it possible to determine the importance of the training examples in Random Forests, analogously to what's done with predictors? Basically the idea would be to find important samples in the data, ...
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2k views

Multiclass vs. One-vs-All vs. One-vs-One classification

I am working on a classification problem with 7 classes. Is there any rationale to suspect that the best model might be found with a multiclass classifier, multiple one-vs-all classifiers, or even a ...
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512 views

Difference between training and test data distribution

The basic assumption in machine learning is training and test data follows same distribution. But in reality this is highly unlikely. Covariate shift address this issue in which training and test ...
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1answer
5k views

Micro vs weighted F1 score

In a multi-label or multi-class classification setting, when choosing between a micro or a weighted F1 score, what shall I take into account? The main upside of choosing macro is that one gets a ...
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1k views

How to draw plot of the values of decision function of multi class svm versus another arbitrary values?

I am trying to draw a plot of the decision function ($f(x)=sign(wx+b)$ which can be obtain by fit$decision.values in R using the svm function of e1071 package) versus another arbitrary values. From ...
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173 views

False positives and False negatives of three or more classes

I have a $10\times 10$ confusion matrix $M$ generated after to execute an KNN classification process for digits recognition (0,1,2...9). As usual, each row of $M$ represent the "true/real" class of ...
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How to use information about likelihood of classes in a classifier?

General question: How can information about the likelihood of classes be used to improve a classifier? Suppose that the probability of each class is known quite precisely (from a very large sample), ...
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Student classification with Multinomial Logit

I’m analyzing student performance data. In my dataset each row corresponds to a student and each column contains several performance metrics (continuous) and the student type (categorical, 4 types). ...
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Who will follow who based on tags?

Suppose users in a system like a social network are described by a number of tags. The number of tags can be assumed to be less than 10. Example John: funny musician geek professor Peter: skinny ...
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66 views

Priors for discriminative methods?

Say we want to build a classifier for a binary classification problem using a discriminative method (e.g. SVM) and be able to impose a prior on the classes. For example, let's assume that we want to ...
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609 views

What methods can be used to transform data?

I am solving a binary classification problem with 4 predictor variables. The variables didn't seem to be linearly separable. I have used Neural Networks and Kernel SVM which work and give desired ...
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208 views

Hierarchical classification

I'm currently working on the classification with massive amount of data. Similar to the kaggle one. Data input consist of features and multiple labels that can be hierarchically aligned. At first I ...
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751 views

Easy to follow tutorial on using Markov Random Fields for classifying pixels in gray-scale images

I am trying to learn how to use Markov Random Fields for classifying pixels in an image. Could someone please direct me to a simple tutorial demonstrating how this is done. The tutorial needs to ...
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Minimum training sample size required for a classifier

What is the best method to determine the minimum number of training samples required for a classifier? I am only comparing one classifier (four class problem), discriminant function analysis (DFA) ...
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2k views

SVM classifier (with soft-margin) implementation in R, gamma value and quadprog

I'm trying to implement a Support Vector Machine classifier in R and I have to solve the optimization problem using the quadprog R package which solves problems of the form : $$min_b \frac{1}{2} b^...
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1answer
233 views

Classification with increasing number of classes

I have dataset with large number of labelled data (say that there are k classes). I have also another, much smaller dataset with unlabelled data that I want also to be labelled. The problem is that in ...
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272 views

Determining conserved features using a Bayesian approach

I would like to perform some sort of binary classification, and my data set consists of 100 examples (for each class), which are vectors with 2500 elements. Ideally, I would like to determine which ...
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122 views

Classifiers with post-training constraints on the prediction space

I'm familiar with using tools like SVMs and decision trees for discrete classification problems. But one detail that I have not encountered in that domain is: what do you do if your classifier must ...
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152 views

Independent variable misclassification and statistical tests

Suppose we have our standard DGP, $y=\alpha+\beta x+\varepsilon,$ where $x$ is binary. Let's say the observed $x$ is actually measured with error, so that the explanatory variable is misclassified for ...
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Adjusting the classification threshold of Naive Bayes

I've been involved in a machine learning project recently and am now in the process of writing the project up for a paper submission. We used the naive bayes classifier on the project and developed a ...
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311 views

Category selection for text classification

It is said that to achieve a good result (many different metrics) for text classification, it is not always a business of selecting the algorithm/classifier. Sometimes, it is even more important to ...
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506 views

Using QDA for Non-Gaussian distributions

I am evaluating a Quadratic Discriminant Analysis (QDA) classifier on a high-dimensionality feature set. The features come from highly non-Gaussian distributions. However, when I transform the ...
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500 views

Unlearning Neural Network? Prevent learning from a specific feature

Is it possible to train a NN to avoid the features that a different neural network finds? For example, let's train a simple 1 layer CNN with 1x1 kernels on a supervised binary classification problem. ...
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2answers
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multiclass classification having class imbalance with Gradient Boosting Classifier

I am using Abalon data for classification from UCI(https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data). I have scaled data and used TSNE for visualization. ...
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1answer
768 views

Test for Statistical Significance in the Accuracy of a Machine Learning System

I have what I imagine is an elementary question about evaluating statistical significance, but while I know a lot about probability I can't t-test my way out of a paper bag. From here I'm hoping to ...
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Figuring out the margin for the soft margin SVM (exam question)

This is an exam question and I am not sure whether it is solveable with the given information. We were given a graphic that displayed binary labelled points $x^{(i)}\in \mathbb{R}^2$ with $y^{(i)} \...
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1answer
34 views

How to model the correlation between predictions in classification problems

In most of deep learning classification problem settings, the predictions $p(y|x)$ are often modeled to be independent, namely, $$p(y|x) = \prod_{i=1}^{N} p(y_i|x_i)$$ However, this assumption may ...
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1answer
70 views

Combining many sparse binary variables

Based on kjetil b halvorsen suggestion, I rephrased my problem: My problem is analogous to the following: i am supposed to predict if a high school student will go to university (Yes/No). I have ...
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Question about using Bayesian rule as a classification for continuous data set

Please note that my question is not about coding. I am now learning Bayesian classification and I think I understand it in a discrete case. I have trouble understanding it for multivariate continuous ...
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61 views

Is there any strategy for validating the result of a general comparison between several confusion matrices?

Disclaimer: Recently we have developed a python library named PyCM specialized for analyzing multi-class confusion matrices. A compare system has been added in <...
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361 views

Overfitting in Random Forest Classifier?

I would like some help from you in a classification model that I am developing. In summary, the problem is: – Classification problem with binary outcome (0/1) – The classifier is a Random Forest ...
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1answer
63 views

what is the diffrences between online and one pass learning?

as long as I know, online learning takes actions at each time step (for each data), and one-pass algorithm just can see each data once. I already read Wikipedia: about streaming algorithms. These ...
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103 views

k-means clustered data: how to label newly incoming data

I have a data set with labels that were produced by a k-means clustering algorithm. Now there is some data (with the same data structure) from another source and I wonder what is the most sensible way ...
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Applying boosting to predictions from a Random Forest

I have a class of datasets for a binary classification problem where it is known that Random Forest performs poorly compared with GBM or FFNN, rarely adding anything to an ensemble. I've had an idea ...
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1answer
258 views

Correct cross-validation procedure for single model applied to panel data

Questions What is the correct CV procedure for panel data? I've been thinking of the problem as cross-validating a model fit to multiple time series data. Is the "population informed" CV procedure ...
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311 views

Calibration of penalized (LASSO or ELasticNet) logistic regression models

I would be very grateful for any help me with the following general query regarding calibration of penalized models with a binary outcome. I would like my prediction model to be calibrated (mean ...
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299 views

Measure agreement among experts in multi-label classification task

I was wondering whether there is a metric that can be used in order to compute the agreement, and therefore something like an upper bound for classifiers, among expert-labelled data. Assume there is ...
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Should I use statistical tests (e.g., Hosmer-Lemeshow) to assess predictive models?

Generally, is it useful to carry out statistical calibration tests on purely predictive models? For instance, if I build predictive model and I choose final model relying on cross validation results (...
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1answer
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Dynamic interactive learning

I am trying to solve a classification problem where I have a set of known X values. I know the classification objective i.e. the discrete set of values the Y can take. However, I don't have any ...
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If I have enough data, can I just model unbalanced classes?

I am tasked with understanding what's causing a rare manufacturing failure (ca. 1 in 5000) to worsen recently (to ca. 1 in 3000). I have a very large database, so I can get sufficient samples from ...
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How to handle machine learning inputs that should be considered as set of vectors, but whoes interpretation is order invariant (order agnostic set)

Basically wondering best practices for input modeling and ML algorithm type(s) for inputs that essentially model samples that are a bag/set of "sub-objects", so order does not matter. Think of the ...
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Why use separate trees for each class in multi-class gradient boosting?

Gradient boosted decision trees can be used to solve multi-class classification problems. Friedman (2001) fit $K$ trees on each iteration—one for each class. Multiple GBM implementations also follow ...
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Supervised document classification with prior distribution on some features

There is a somewhat off-the-beaten-path supervised document classification problem I am trying to tackle. For the sake of simplicity, let's say we are using the bag-of-words approach. Usually, we ...
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1answer
370 views

Combining classification and anomaly detection

I want to build a system, that can classify known classes in a supervised way and at the same time tells if there is a new anomaly class it has not seen before. The user can then label that unknown ...
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432 views

What is a good loss function in multiclass multilabel classification where only one of the possible labels is observed?

I am training an ANN in multiclass multilabel scenario, where only one of the possible labels is observed at a time, let me illustrate on an example: I have a state X and the ground truth label Y for ...
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Voting between classifiers : How to prove it works?

Assume m independent binary classifiers with probability $p$ to be correct $p>0.5$. Show that the probability of a voting, e.g. decision is made by the majority of classifiers is correct with ...
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Using confusion matrix to improve my SVM

I ran an SVM classifier on the CIFAR_10 classification workbench. I got about 2/3 accuracy (which I think is Ok, but I want to improve...) Here is my confusion matrix: ...