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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How does a Relevance Vector Machine (RVM) work?

Relevance Vector Machines (RVMs) are really interesting models when contrasted with the highly geometrical (and popular) SVMs. In the light of a question like How does a Support Vector Machine (SVM) ...
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How to compare the accuracy of two different models using statistical significance

I am working on time series prediction. I have two data sets $D1=\{x_1, x_2,....x_n\}$ and $D2=\{x_n+1, x_n+2, x_n+3,...., x_n+k\}$. I have three prediction models: $M1, M2, M3$. All of those model ...
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How can I get feature importance for Gaussian Naive Bayes classifier?

I have a dataset consisting of 4 classes and around 200 features. I have implemented a Gaussian Naive Bayes classifier. I want now calculate the importance of each feature for each pair of classes ...
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inferring most important features

Given a set of $n$ instances. For each instance I have a feature vector consisting of $m$ (numerical) features ($x_1$, $x_2$,...,$x_m$), n>>m. Moreover, for each instance I have a numerical score $y$ (...
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Using ML to assist human labelling in dataset with highly unbalanced classes

Are there scientific issues with using ML to assist human annotation? I've got a 3 class unlabelled dataset where only 1 in 500 elements belong to the 2 classes of interest. The labels arn't ...
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363 views

Baseline for Precision-Related Metrics

When working with ROC-AUC as a metric for binary classification, one often considers a value of 0.5 as a baseline from a random classifier (i.e. a data-blind classifier that randomly classifies test ...
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582 views

Machine learning with ordered labels

The usual method for adapting binary classifiers like various SVMs to multilabel data is one-vs-all, which assumes that labels are independent and in case of a prediction error we don't care what ...
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1answer
842 views

Computing a bootstrap confidence interval for the prediction error with the percentile and the BCa method

I have two related questions regarding the computation of a non-parametric bootstrap confidence interval for the prediction error. Setting: I have a sample S from a data population P and a learner L, ...
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1answer
634 views

Random Forest: Class specific feature importance

I'm using the bigrf R-package to analyse a dataset with ca. 50.000 observations x 120 variables, classified into two groups. After growing a forest of 1000 trees, ...
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415 views

Convolutional neural network for multi-variate time series?

I want to use CNN architectures for classification of multivariate time-series, where we apply one label to each sequence. I searched the net for the available designs in the literature and i found ...
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683 views

Why decision boundary differs between multinomial (softmax) and One-vs-Rest Logistic Regression for multiclass classification

Can someone please explain why the decision boundary differs between multinomial (softmax) and One-vs-Rest Logistic Regression for multiclass classification. Example shown below http://scikit-learn....
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Optimising dual SVM: why do some authors drop constraints?

In Hastie's Elements of Statistical Learning the dual problem is put as $$ \begin{align} \text{arg min}_\alpha \quad &\ \frac{1}{2}\alpha^\top Q\, \alpha_i- \sum_i \alpha_i\\ \text{subject to}\...
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1answer
621 views

Multi-label classification: Predict product category

I want to predict to which product category a product belongs. A total of 400k products need to be translated from the old (less refined) to the new product category tree. (E.g. alarm clock used to ...
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why use diagonal $\Sigma$ when working with Bayes decision theory?

My prof. said in the class that for Bayes decision rule, the likelihood is Gaussian and in practice, we will almost always work with a diagonal $\Sigma$. Why is that? I know that a diagonal $\Sigma$ ...
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2k views

Precision and recall of a random classifier

My understanding of precision and recall tells me that there is a tradeoff between these two measures: you can improve one at the cost of the other. However, when I think of a random classifier (on a ...
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595 views

Reasons for transforming multiple class classification problem into a set of binary sub-problems?

Does anyone know of a good reference that list the reasons for transforming multiple class classification problem into a set of binary sub-problems? In response to comment: One reason to transform a ...
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Computation of log-likelihood in semi-supervised naive bayes

I have the following 2 questions about log-likelihood computation in semi-supervised Naive Bayes. I have read on several documents online that, in every EM iteration of the semi-supervised Naive ...
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773 views

Classification of multiple time series and case level attributes

I'm pretty new to machine learning so wondering whether someone can help check my thinking or point me in the right direction! I need to create a classifier which can predict an outcome for a person ...
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Maximum entropy classifier and sentiment analysis

I am doing a project work in sentiment analysis (on Twitter data) using machine learning approach. In order to find the 'best' way to this I have experimented with naive Bayesian and maximum entropy ...
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2answers
536 views

How to subset alternatives in nested multinomial logistic regression?

I am trying to predict whether or not captains in a particular groundfish fishery choose to fish on any given day and what variables may influence that decision. Originally I had planned on using ...
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“Hierarchical” Random forests?

Background I am using Random Forest to classify ~900 objects based on a large number (> 80) predictors. I split these 70:30 for training and testing. The overall model does fairly well, giving an ...
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Confusion matrix for multilabel classification

I know that a similar subject was treated here, but my question is a little bit different. I have a result of multilabel classification, like this (2 observations, 3 labels in the example, in ...
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143 views

When is there a free lunch?

The no free lunch theorem (NFL) states that Theorem (Wolpert and Macready 1997) Let $A$ be any learning algorithm for the task of binary classification with respect to the $0−1$ loss over a ...
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When is oversampling poor practice?

For my particular domain and problem, I have data on the entire population. However, my "event" only occurs in 0.5% of the cases. I want my model to be able to pick up on significant characteristics ...
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1answer
133 views

Are there better estimators of misclassification error than the fraction of misclassified test points?

Assume we train a binary classification model using the training set. Also assume that the model returns an estimate of the probability of success $\hat f(x)$ for every feature vector $x$ and was ...
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TensorFlow Deep MNIST for Experts tutorial: kernels seem to never learn anything

I'm following Google's TensorFlow Deep MNIST for Experts tutorial. Here is my code: http://pastebin.com/ePktssrn The networks seems to get close to 100% accuracy after about training 1000 steps, ...
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779 views

Probability distribution over classes as labels in classification task

Classical classification problem has next formulation. Given a set of $n$ attributes, a set of $k$ classes and a set of labelled training instances: $(i_i, l_j),...,(i_j, l_j)$, where $ i = (v_1, v_2,...
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True positive, false negative, true negative, false positive definitions for multiclass-multilabel classification?

I'm trying to apply some evaluation metrics to several clustering methods. I thought that I knew them basing on the multiclass confusion matrix, considering the rows as the actual classes and the ...
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1answer
1k views

Combining multiple classifiers

I am trying to do a binary classification of text articles into {relevant, non-relevant}. The text articles have following features: [[article text, ...
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logistic regression prediction: changing interpretation with changing prior

The data include 3 equally sized subsets A, B and C, belonging to two classes: A belongs to class 1. B and C belong to class 2. The prior probabilities of an observation coming from class 1 ...
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1answer
328 views

Is it helpful to have monotonic features when using a random forest for classification?

I am training a random forest for binary classification. Here is a plot of one of my features, which is an integer giving the number of months since an event. The y-axis gives the proportion of cases (...
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942 views

Logistic regression and maximum entropy

I have read (e.g. here) that a (multinomial) logistic regressor corresponds to a maximum entropy classifier. My question is, how does one end up with the formula for logistic regression starting with ...
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k-fold cross validation vs k times hold-out validation

I am facing the evaluation of a genetic programming algorithm. I am using the Proben1 cancer1 dataset to evaluate the models created by this algorithm. This dataset contains 699 samples, which is ...
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Estimating parameters using Kullback-Leibler or Kolmogorov-Smirnoff via Nelder-Mead

I want to find the parameters of a model which specifies a set of classification probabilities, for say M classes. (I'll use the parameters in another model later.) Given a set of parameters $\theta$,...
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Transferring to new domain

Suppose I have a set of characters with both natural scene and synthetic images, and another set with only synthetic images. I'd like to make a classifier which is trained on only on this data and ...
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460 views

Number of states and symbols in multi class Hidden Markov Model classifier

I'm designing a multi class classifier (for 4 classes) using Discrete HMMs with States N and Symbols M for each of the HMM. However, I found that recognition performance(i.e highest log likelihood) ...
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173 views

Asynchronous data stream matching

Suppose you have a classifier $C^n$ which continuously outputs a stream of classification labels $K^n_i$ and corresponding timestamps $T^n_i$. Also, we know the prior probability $P(K^n) \forall n$. ...
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How to model probabilistic inputs with continuous output using regression

I have trained a multi-output classifier that takes an image as input and returns softmax logits as output. To be specific, the multi-output classifier takes an image and says the probability that ...
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How to combine noisy and noise-free datasets to train a model

Overview Suppose I have two datasets, both of which consist of rows of features and their matching labels. One of these datasets is noise-free and its labels correspond to the ground truth, but the ...
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PCA, SMOTE and cross validation - how to combine them together?

I was reading a lot recently about PCA and cross validation and it seems that the majority call it malpractice to do PCA before cross validation. I would also like to perform SMOTE, but there is a ...
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Can F1-Score be higher than accuracy?

I'm using sklearn's confusion_matrix and classification_report methods to compute the ...
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1answer
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Should we balance the data set if the data is intrinsically unbalanced?

Say I want to predict the cancer rate(regression)/predict the whether a person has cancer or not(classification). The data intrinsically has few cancer patients/low cancer rate, say 1/200. And the ...
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How does one design a data set from polynomial target function such that logistic regression separates the data perfectly?

I want to design a target function for a classification task of the form: $$ f_{target}(x) = \mathbb{1}_{>0}[\sum^{D^*}_{i=0} w^*_i x^i] = \mathbb{1}_{>0}[ \langle w^*, \Phi(x)\rangle]$$ and ...
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Effectiveness of Standardization and Normalization in Machine Learning

I am just studying the basics of machine learning and had a question about the standardisation and normalisation of the features and its effectiveness. I have read this CrossValidated question and ...
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Adversarial examples - regularization method

In Intriguing properties of neural networks (https://arxiv.org/pdf/1312.6199.pdf) they show (4.3), that the existance of adversarial examples is closely connected to the upper Lipschitz constant, ...
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How to include negative examples in multi-class classification?

I have a problem similar to this question: How do I use negative examples (in addition to positive ones) for training a multiclass softmax classifier (or a neural net with softmax output)? where I ...
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classification: concatenating descriptors vs. using multiple classifiers

Consider a typical machine learning problem where you try to do object classification from a high-dimensional set of features. Suppose we know that the features are actually a collection of distinct "...
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Google gender-pay gap vs

Background: I read this: google schools US government about gender pay gap. It derives from this google blog post by Eileen Naughton, VP of People Operations. She asserts that google is somehow "...
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Unsupervised Anomaly Detection Threshold Selection

If we have a data set that contains only positive examples I am wondering how we can effectively choose a threshold for an anomaly detection technique. Are there anomaly detection techniques that can ...
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280 views

Stop Word removal with RNNs

This might be a dumb question, but 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 "...