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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15 views

Which classifiers work well with unbalanced data?

I have a binary classification problem which is very unbalanced - it can have 98% of data from one class. Which classifiers work well with this sort of data? I have an unlimited supply of training ...
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0answers
14 views

Standardizing before/after/at all when using multi-class LDA for pre-processing step

If a multi-class Linear Discriminant Analysis (or I also read Multiple Discriminant Analysis sometimes) is used for dimensionality reduction (or transformation after dimensionality reduction via PCA), ...
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2answers
27 views

Good machine learning models for confusable categories

I'm using the word confusable to represent similar looking glyphs in text. I'm building an optical character recognition tool with the primary goal of experimenting with machine learning – especially ...
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1answer
42 views

For a model like this what performance measures can I calculate and how?

Methods: From the machine learning literature, I understand different parameters can show performance of model in machine learning. I would briefly expand my understanding with confusion matrix: ...
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2answers
110 views

High precision with low recall SVM

I'm classifying a data set using SVM and those are the precision and recall values for two classes. ...
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1answer
16 views

SVM Classification with Duplicate Training Instances

I'm using SVMs with linear kernel for sentence classification (binary). My dataset contains many duplicate instances i.e. many sentences in the training set have identical feature vectors. In the ...
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11 views

Binary classification with too few positive samples

I met a problem of doing a binary classification with quite few positive samples. For example: Binary classification with either labelled 1 samples (positive) or labelled 0 samples (negative ...
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3answers
48 views

Is LDA more likely to be overfitted than SVM?

I went to a short talk and the speaker quickly mentioned something like 'LDA is more likely to be overfitted than SVM'. Is this true? And why? Thanks a lot. A.
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14 views

How many features can be used for classification?

Asked a similar question the other day without an answer Link. I think maybe the question there is too big. Here I want to ask a specific one: 2 Class labels (Binary classification labelled with ...
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0answers
10 views

Is there an effect size for Kappa's?

I am staring a project on comparing standard ways of creating a classifier with some heuristic methods. The heuristic methods should result in a faster training for the classifier but should result in ...
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0answers
12 views

Post-process the output of a Multinomial Naive Bayes text classifier

I have a multinomial text classification application where there are other features than the words in text which can be useful to do the classification e.g, contains email address, contains an URL, ...
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1answer
12 views

Quantifying Change in a Histogram Valued Timeseries

I'm attempting to do binary classification where my raw features are collections of histograms that are recorded in a time series. These histograms are scaled to sum to 1. To be more precise and ...
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3answers
26 views

Kernel selection using SVM for keyword frequency classification

I have data in Weka .arff multiple-class training and testing data representing daily word frequencies in RSS feeds as follows: ...
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1answer
49 views

Simple SVM Question

For a linear SVM, the documentation tells me the formula is: $$ \frac{1}{2}w^Tw+C\sum\limits_{i=1}^l\xi_i$$ Please explain to me in layman's terms what w (and ξ) represent. Is w the distance to the ...
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0answers
12 views

Getting the rule from cross validation

I've got a question. Let's say I have a medical data representing 2 classes of patients (healthy and unhealthy) and some number of predictors which characterize these patients. Choosing different ...
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0answers
24 views

How many features to overfit the classification?

Recently, I have got some 'strange' comments from the reviewer of my paper. In my paper, I discussed a novel feature extraction method, and then I compared three classification methods for my binary ...
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1answer
12 views

should weights be scaled too?

I am using supervised learning algorithms (specificly SVM) on my data. I know that scaling was needed for my input data. however as I am also adding weights (using pairwise comparison), I am not sure ...
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1answer
32 views

Time Series Data Mining Library?

Can anyone recommend a library for time series data mining tasks other than predictive modeling and statistical analysis? There seem to be a number for these purposes (e.g., Gretl), but nothing for ...
0
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1answer
35 views

test validation versus k-fold cross validation

I am attempting to use a neural network, after using other machine learning algorithms. I am using the RSNNS package (I am willing to use / evaluate other packages) that's part of R. I would like to ...
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0answers
6 views

Maxent Markov models in R

Is there a package that implements Maxent Markov Models in R? (http://en.wikipedia.org/wiki/Maximum-entropy_Markov_model). I understand that package crf implemets conditional random fields which are ...
1
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1answer
31 views

A clustering and classification question

I'm trying to classify my set of data into two classes (introvert / extrovert). I was thinking of using a decision tree at first, but I don't have any potential known results in order to create my ...
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0answers
16 views

Softmax regression or $K$ binary logistic regression

For a multi-class classification problem, we can use $K$ binary logistic classifiers, or one softmax regression classifier, so how to make the choice between the two? IMHO, the $K$ binary logistic ...
3
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1answer
44 views

Multi-class classification via all pairwise classifications with LDA

I have trained linear discriminant analysis (LDA) classifiers for three classes of the IRIS data and struggling with how to make the classification. Here is the procedure: For the Iris data, I have 3 ...
0
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0answers
9 views

Is it possible that boosting doesn't increase the predictive power of tree?

I have a data set of 282 observations, and my response variable is a binary variable where 0=normal and 1 =disease. I constructed a classification tree with rpart ...
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0answers
46 views

Multicollinearity classification regression with R [closed]

I want to do multiple regression, but my explanatory variables are highly correlated with each other. Some are roughly a linear combination of others. To deal with that, I tried to analyse my data ...
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1answer
29 views

Classifying a set of photos to places

I want to cluster photos and map them to places. As input I have Photos with locations (lat, long) Places - some as (imprecise) bounding boxes, some just as points, maybe others as bounding ...
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0answers
6 views

Liblinear logisitic regression with L2 regularization for classification

I am trying to use the liblinear library for logistic regression with L2 regularization. However, I am finding some issues with it. For eg when choosing the cost parameter, I chose the C parameter to ...
0
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0answers
10 views

Exposure classification bias, post-modeling

I am building a high-dimensional Bayesian spike-and-slab model to study the association between several organic compounds and a continuous outcome. The goal is to select the most influential compounds ...
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0answers
22 views

synthetic replication of a fund using a classification of the univers and a recursive regression [closed]

i m working on a replication of a specific fund i have daily data of this fund, and 20 index to replicate this fund what i aim to is giving this data i want to know what is the starategy of the 5 ...
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1answer
21 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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0answers
9 views

Which classifier is the best for short text classification, Naive Bays or RBFN? [on hold]

I am doing my M.E. project on short text classification for Online Social Networks.I am using Weka for classification.Please tell me between Naive Bays and RBFN which classifier is the good one for ...
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0answers
11 views

Naive Bays classifier showing results for precision, recall and F value is always 1

I am implementing Naive Bays text classifier using Weka. I have trained it with very few words (about 20). I am getting the result that precision, recall and f value all as 1. Is this possible? ...
0
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0answers
11 views

SVM Numerical example (step by step)

I have a constant problem understanding SVM for both linear and non-linear separable cases. I understand upto a point that SVM establishes a hyperplane that has the maximum or optimal distance between ...
3
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3answers
51 views

Recognition of simple patterns and prediction

I have been doing supervised learning and classification with multilayer perceptron for some time. But now I need to use unsupervised learning to infer the presence of a pattern and I would need some ...
0
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0answers
18 views

How to check discriminant analysis assumption in R using lda?

I want to use lda in MASS package in R. According to the theory behind that, first need to validate the assumption. Actually I've found some example from the net but they did not bother to validate ...
2
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3answers
143 views

Does k-NN with k=1 always implies overfitting?

I found somewhere such statement, but on the other hand in some sources I found, that it is ok. What about risk of overfitting while using 1-NN in binary classification problem where explanatory ...
1
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1answer
18 views

Plotting Scatterplot Matrix or Correlation matrix or both?

I have a problem where I want to use a classifier for it. So I defined a set of features and created a dataset. Now I want to generate some plots to understand the features. I came across the ...
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0answers
17 views

How to fight underfitting in deep neural net

When I started with ANN I thought I'd have to fight overfitting as the main problem. But in practice I can't even get my NN to pass the 20% error rate barrier. I can't even nearly beat my score on ...
2
votes
1answer
75 views

What is the difference between a multi-label and a multi-class classification?

What is the difference between multi-label classification and multiclass classfication. Speficially, what is the difference between a label and a class? Please provide a clear example. "Multiclass ...
0
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0answers
11 views

Down-sampling with building models (specifically random forests)

I was wondering if anyone had ever used down-sampling to build random forests with data that has unbalanced classes. Basically down-sampling samples (with replacement) x*min from the population where ...
0
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1answer
28 views

advantage of euclidean distance for classification

Has euclidean distance any advantage in compare to another distance based methods like Manhatan distance or Maximum difference metric?
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0answers
9 views

Feature selection: all features vs a subset of them

I am doing a binary classification. The dataset has 3000 samples, and each sample has 10 features. But I find that the performance of using all 10 features is almost the same as that of using only the ...
1
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0answers
21 views

Binary classification of dated text documents with seasonality

I have a collection of training documents with publication dates, where each document is labeled as belonging (or not) to some topic T. I want to train a model that will predict for a new document ...
0
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1answer
25 views

Unbalanced test data matter?

I have balanced training data: 300 positives and 300 negatives, but the test data is unbalanced: I have 15 positives and 60 negatives. Will the unbalanced test data impact classification accuracy? ...
3
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2answers
73 views

Bayes decision theory: Classification error probability

In Bayesian decision theory: Given $\omega_1$ and $\omega_2$ as two classes for classification, $P\left( \omega_1 \right)$ and $P\left( \omega_2\right)$ their prior probabilities, $x$ the feature ...
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0answers
8 views

Classifying a text into question or answer

I'm searching for a library or tool that allows me to classify emails of a mailing list into what mails are very likely questions and what mails are very likely answers. Can anybody recommend such a ...
0
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0answers
9 views

Which method to find which features explain the target?

I have many categorical and numerical features and I sub-selected a few of them by a Random Forest. Now I'd like to determine which features probabilistically(/causally?) explain the target. Something ...
2
votes
2answers
48 views

How to get rid of bias in data?

I have been trying to classify a set of data into one of four classes. The data has already been generated and I have set aside 10,000 for training and 2,000 for testing. I have also generated the ...
0
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0answers
6 views

Different Scoring with libsvm

I have been using libsvm for binary classification, e.g. given data $(x_1,\dots,x_n)$ and labels $(y_1,\dots,y_n)$ where $y_i\in\{0,1\}$. Now I am wondering if the prediction of my model can be tuned ...
0
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0answers
17 views

Creating classification features from wavelet transformed time series

I'm interested in using a wavelet transform, Haar for example, to create classification variables from time series data to use in logistic regression. Simple example. Let's say I'm trying to predict ...