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 ...

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Learning from sparse label products

Consider the following binary classification problem $f(\mathbf{X})\rightarrow\mathbf{Y}$ where: $\mathbf{X}$ = Feature matrix $\mathbf{Y}$ = Product of several label (binary) vectors, i.e. ...
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11 views

Analysis to assign a categorical var to a ordinal

I have a categorical outcome and a categorical predictor with many levels. Which test can I use to assign to each predictor level the probability of being in one of the classes of the outcome (with ...
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9 views

Generating text data for training for doing named entity recognition and extraction

I'm trying to build an algorithm for doing named entity extraction. It goes like this. There is a large set of text documents [communications], from which specific information has to be extracted. The ...
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1answer
14 views

ordinal classification using C5.0

My question is about machine learning to predict ordinal variables. Most ML models for classification that I have seen do not make any assumption about the order of different categories. I can see ...
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1answer
15 views

Text Classification using TfIdf and Bernoulli NB

So, as I am reading about Bernoulli distribution and text classification, I want to understand how Bernoulli uses TfIdf features? Since TfIdf values are within [0-1) but Multivariate Bernoulli assumes ...
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7 views

Define feature for text classification using NLTK [on hold]

I'm working on Aspect Based sentiment analysis , I have a training set (text ,and aspectTerms) for each review. Using NLTK , I wan to build a NaiveBays Classifier that predict aspects of test unseen ...
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13 views

Clusters as input for classification

I'm currently performing clustering as a batch job and then in real time I'm assigning new points to cluster whose centroid is closest to new arrived point. The other approach that I see is to ...
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22 views

Maximum Entropy classifier, high precision but low recall

I'm working on a sentiment analysis study of twitter data using the Maximum Entropy classifier. I've gathered dozens of thousands of tweets. To produce features, I used unigram, bigram and dictionary. ...
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2answers
37 views

Classification Algorithms

Rather than due a google search and hope to find "the best" or an exhaustive list of classification algorithms I thought it might be easier to pose my questions here, and then get advice from you ...
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28 views

Statistical quality criteria for classifiers and recommenders

I want to know several points regarding the evaluation of data sets. I would like to know which metrics are the best for the evaluation of: a) recommenders and classifiers b) online and offline ...
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36 views

Linear regression for classification

Suppose, I have a classification problem with 2 classes (0 and 1) and evaluation criteria is AUC. I used the following method: fit a linear regression and then pass its predictions through the ...
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6 views

How to shorten the detection time of adaboost algorithm?

I'm working on a license plate detection using OpenCV's adaboost algorithm. However, after training, it shows that the detection takes 3200ms for a single image, where the image size I used is ...
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9 views

Classification for series of lat, lon points of varying lengths

I have a dataset of series of latitude and longitude points. Each series of points corresponds to some activity. The number of latitude/longitude points is not the same for the series. My dataset ...
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1answer
25 views

Importance of McNemar test in caret::confusionMatrix

There are many metrics to evaluate the performance of predictive model. Many of these appear relatively straightforward to me (e.g. Accuracy, Kappa, AUC-ROC, etc.) but I am uncertain regarding the ...
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14 views

Is there a non-parametric repeated measures test for replicated block data?

Following on from an earlier question, I've got another question about comparing machine learning classifiers on various datasets. From the response to that question, and also to this paper I ...
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1answer
146 views

Statistical significance test for multiple binary classification problems

Let $C_1$, $C_2$ be two binary classifiers, which are used to classify some data (images, videos, etc) to $30$ different classes, using an one-against-all approach. Then, we have two $30$-dimensional ...
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19 views

One-class SVM vs NN with backprop… Or is there something better?

I'm pretty new to unary classification, so I've been playing around with different approaches to one-class document classification in Python. NN seemed promising at first, but has some undesirable ...
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1answer
29 views

How to use geometric proximity in classification

I am doing a classification of certain regions of an image. Let's say I have done the classification, and some classes have been classified positively (negatively) with high probability. For my ...
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7 views

Image analysis classification where images are of different dimensions, resolutions, etc

I am working on an image classification problem where I have black and white images of all different dimensions and resolutions. The images belong to 1 of 5 groups including an unknown category. These ...
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1answer
16 views

Minimum number of tested patients to have a reasonable ROC curve [closed]

What are the minimum number of tested patients and the acceptable prevalence percent required to have a reasonable ROC curve? for example, can I test a total of 16 patients, 5 are diseased and 11 are ...
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8 views

Classification problem with constraints

I am trying to solve a classification problem with constraints and need advice on how I should approach it. Here's the problem: Given N observations, FLAG_j, j=1,..,N (this is a binar variable), and ...
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13 views

Significance of multivariate models and correction for multiple comparisons

I have performed a multivariate binary classification using a number of features (or variables), I will call them features from sets (A), (B) and (C). I have calculated the P value of this ...
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2answers
30 views

Class Imbalance

What are the best practices for fitting a binomial classification model when the classes are very imbalanced? For example, 99.9% 1's and 0.1% 0's.
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22 views

K nearest neighbour [duplicate]

*NOTE, this question has been asked before here, yet I wasn't any less confused after reading the answers. * Is the k-nearest neighbour algorithm a discriminative or a generative classifier? My first ...
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7 views

max margin vs max posterior/likelihood advatages

I am working on some parameter learning approaches for image classification. What is the differences between the following two for image classification? max margin methods maximum likelihood/ ...
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1answer
27 views

Is up- or down-sampling imbalanced data actually that effective? Why?

I frequently hear up- or down-sampling of data discussed as a way of dealing with classification of imbalanced data. I understand that this could be useful if you're working with a binary (as opposed ...
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13 views

Automatically classifying user activity/sessions on a website?

X-posted from Stack Overflow: I have a large body of records pertaining to user activity on a website. What I want to do is some sort of classification on each user as they navigate my website. Every ...
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1answer
10 views

Automatically classifying user activity/sessions on a website?

I have a large body of records pertaining to user activity on a website. What I want to do is some sort of classification on each user as they navigate my website. Every algorithm I found so far uses ...
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1answer
48 views

LogisticRegression - binary classification, “custom threshold”

I have a binary classification problem that I am trying to solve with sklearn's Logistic Regression. I am aware of the fact that the predict_proba() function is apparently only an approximation of the ...
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1answer
43 views

Understanding RPART model results

I have operational fault data and maintenance data. The operational fault data was used to determine if the maintenance improved the fault indicator (true/false). The maintenance data was used to ...
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0answers
26 views

Binary classifier via Mahalonobis distance

In a recent conversation with a colleague at univerity, they mentioned that for a certain problem, we can "just use a binary classifier". When I inquired as to how they would train, they said "No ...
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47 views

difference between “gold standard” and “ground truth”? [closed]

What's the difference between"gold standard" and "ground truth"? Or are these two concepts the same?
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23 views

Model for the response of campaign in generalized linear model

Part of this question concern actual case and other is hypothetical case. Suppose that agent calls list of leads and offers them particular product. Agent records response in the CRM system which ...
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0answers
7 views

Plotting Classification Tree with a lot of Factors - Legend Option?

I want to plot a classification tree and display it nicely. The problem is, because my factor variable has a lot of levels, the node displayed would look something like this: State Name = alabama, ...
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22 views

Time series classification and auto correlation

I am trying to understand time series data and data mining. I am trying to classify EEG data set. The classes are known in advance for the data set and the algorithm is trained on the example data ...
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9 views

positive and negative sample count for ConvNets

I have been trying to set up a ConvNet to classify some data. This data should be classified to either 1 (being what I need to get from the image) and 0 for everything that is irrelevant. I have ...
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16 views

What is “fitted function” in the context of boosted regression tree?

I'm following the tutorial of package dismo's boosted regression tree, which produces two graphs, about fitted function and ...
4
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1answer
101 views

Justifying and choosing a proper scoring rule

Most resources on proper scoring rules mention a number of different scoring rules like log-loss, Brier score or spherical scoring. However, they often don't give much guidance on the differences ...
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2answers
68 views

SVM heavily over fits the data (classifying Highly Unbalanced data )

I have a huge training set from which I am supposed to regress and classify, i.e I classify whether an event will occur or not and another task is to regress the intensity of the event in future. The ...
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1answer
14 views

Affect of Misclassification Cost on SVM

I am using Matlab to train an SVM for very unbalanced data. However, my concern is not so much for the particular class assignment (ie 1/0), but rather to the scores (the prethreshold continuous SVM ...
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1answer
15 views

What does it mean to say that two classifiers are independent?

For example: http://www.tandfonline.com/doi/abs/10.1080/00031305.2013.778788 Is it simply that neither classifier uses the output of the other?
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17 views

The best algorithm for short documents clustering

I have a corpus of short text documents. Each document is an automatic recognized phone conversation (a dialog) from a large call center. The texts are not clean and have lots of grammar and other ...
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1answer
25 views

Association rules or classifier for product modeling for queries

I have a set of products P {1...n} which are rated on a goodness scale G ={1...100} (G10 is more good than G5). Each product has a set of features F {1....m}, now I want to learn a model for ...
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1answer
22 views

Novelty and Outlier Detection for Multi-label Data

I met a problem of using novelty and outlier detection for my multi-label data. For example, I have got some training data that is not polluted by outliers. However, the training data are with ...
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1answer
36 views

Plotting decision boundary of Logistic Regression (liblinear)

I have liblinear model file for a classifier learned using logistic regression. In the file, they say, the weight vector and intercept term. But when I simply plot it as $$w^Tx + b$$ on the original ...
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2answers
23 views

Kernel SVM on sparse data

I have a sparse dataset where a lot of the columns (features) contain mostly zero values. Class labels are multiple discrete categories (10 classes to be precise). I'm wondering if this should trouble ...
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1answer
15 views

Nonlinear Dynamic Online Classification: Looking for an Algorithm

I have two predictors a,b that I want to use combine to classify data. a is stable, it will always produce the same prediction for the same input. b will change and probably improve in time (because ...
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1answer
11 views

how is the affect of ordering the features in perceptron?

I Have 2 linearly separable classes and I have performed a simple perceptron for finding the classifier's threshold. Since in simple perceptron I used all missclassified points in every iteration of ...
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0answers
34 views

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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2answers
49 views

Balanced datasets in Naive Bayes

In a classification model, it is well known that a desirable situation is that all possible classification classes are evenly represented in the training dataset. Datasets that satisfy this property ...