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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32 views

regression for binary classification

Given a binary classification problem, is there any inherent difference (or advantage) to using a classifier (say a logistic regression) and a regression, where the classes are denoted by 0 and 1 (or ...
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10 views

Extracting fixed-length feature vectors from variable-length time series

I have a classification problem where I would like to develop a binary classifier to classify between two different types of objects, given a time-series (signal) related to that object. The problem ...
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1answer
8 views

Weka java API: Attribute Selection and Cross Validation

Is there a way to perform Attirbute selection(aka feature selection) (regardless of method) only for the training dataset before passing data for Cross Validation ? I currently think that the only ...
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1answer
23 views

Determining the class of a new sequence using Markov chains

I want to use a Markov chain to classify a new given sequence as from model+ or model-. For that purpose first I trained two ...
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0answers
26 views

Suggestions needed about classifier fusion

I'm working on a classification problem which involves two classifier to observe a single event. I'm providing a high level description of the problem without going into the technical details (the ...
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0answers
12 views

Optimal classification model for translating words

I have the following problem: I have a set of English words which I want to translate to Dutch. Of each words I mined a set of possible translations. For example, for the word "Eighteen" I obtained ...
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0answers
6 views

Does it make sense to preprocess using autoscaling followed by standard normal variate

I have spectroscopic data of which this preprocessing clearly works best to classify. I haven't seen many apply it however. Does it make sense to preprocess using autoscaling followed by standard ...
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7 views

Multiclass target detection : N X (1 vs all) or 1 X (N vs all) ?

I am doing a multiclass classification using neural networks. say I have 10 target classes and one null (non-of-the-above-targets). is it better that I train a neural network separately for each ...
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20 views

repeating rare examples in unbalanced data classification

So I'm trying to train a neural network for a rare event detection. based on that i have like 1000 times more examples for non-target (everything else) examples that i have for target examples. So i ...
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0answers
17 views

Numerical Problems in Mixture of Gaussians Classifications

I am doing two-class classification with Gaussian Mixture Models (GMMs). If I understand it correctly I have to build two models $p(x | C1)$ and $p(x | C1)$ for the probability of input $x$ given ...
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0answers
19 views

Best classifiers for large data sets?

I'm working on a data set that contains electricity consumption data. There will be 2-3 features used. I'm not sure if that is all of the features to be used. Also, it will be a really large data set. ...
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6 views

Mislabeled training instance detection and relabeling

I have some text data represented by sparse BOWs features ( ~ 5k features). This data must be classified into (~20) categories, however my training labels data appear to be very noisy (> 20 % wrong ...
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1answer
53 views

Calculating feature probabilities for Naive Bayes

I'm reading "Building Machine Learning Systems with Python" by Willi Richert and Luis Pedro Coelho and I got into a chapter concerning sentiment analysis. There is a whole example about classifying a ...
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0answers
5 views

Sample weights for classification problems

How can certain samples in the training set be prioritized (given more weights) in classification problems? What is the formal methodology to do so?
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1answer
30 views

Detecting a consistent pattern in a dataset via Decision Trees and cross-validation

Assume a classification problem where there are two classes and the aim is to detect a consistent pattern which successfully separates the input dataset regardless of how we divide it into training / ...
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0answers
20 views

In search of a proper similarity function

I'm trying to find the most similar sample between a candidate and a bag of samples. Consider you have a knowledge corpus as follows: ...
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0answers
9 views

Supervised learning algorithm that can be easily retrained with new data

I have a web crawler and i want to be able to differentiate a specific class of website (social networks), from others. My problem is that my starting classified data is really small. What I wan't to ...
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0answers
18 views

Is this a case of semi-supervised classification?

I have a rule-based classifier and I know for sure I can classify my data in a number of N classes. And I have also a "small" dictionary where my rules check (during classification process) for every ...
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0answers
9 views

Unsupervised feature learning from raw text as a previous step for clasification?

I have a corpus of 2500 opinions, is it posible to use scikit´s restricted boltzmann machine implementation to extract a feature vector as a previous step to a classification task?. What aproach do i ...
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0answers
17 views

Normalize data in unnormalized data after normalization

i have some data in numeric. I want to do some classifications method. So i decided to check the normalization of the data. I have normalize my data using weka tools. And i think weka had normalized ...
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0answers
39 views

Gini index vs entropy

If I have a discrete probability distribution $p$ with $K$ classes Gini index = $\sum_{K}$$p_k$(1-$p_k$) Entropy = -$\sum_{K}$$p_k$log$p_k$ Per 'The Elements of Statistical Learning', ...
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0answers
6 views

What is the meaning of “finite sample error control”?

I encountered this phrase while reading a paper which goes like this -- "These methods lack finite sample error control due to instability". Although it might not be important, the paper deals with a ...
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0answers
10 views

Assign attributes / categories to users based on their activity / likes

I have a very practical classification problem for which I need some help. I have a database of users along with their activity / likes for a number of car models. I also have the category each of ...
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1answer
25 views

Evaluating a fixed classifier

I have a classifier that is fixed and wish to evaluate its predictive performance using a test dataset. I'm familiar with the situation (e.g. in k-fold CV) where the data is split and the classifier ...
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0answers
36 views

Hierarchical ordinal regression (or ranking) with prediction constraints on clusters?

I am interested in predicting an ordered outcome of 0,1,2 or 3 (0<1<2<3) for individual responses in a bunch of different clusters. In each cluster $i$ of size $n_i$ there is a single 3, 2 ...
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0answers
37 views

K-fold cross-validation for testing model accuracy in MATLAB [migrated]

I'm having some trouble truly understanding what's going in MATLAB's built-in functions of cross-validation. My goal is to develop a model for binary classification and test its accuracy by using ...
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0answers
11 views

How to choose negative training sample for Classification problem

Choosing positives sample is a relative straightforward task, but I'm having some problem on determine what should I use for the negative example. I'm working on a SVM binary classificator, trying to ...
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0answers
19 views

Constructing Random Forests for binary classification by minimizing entropy

I'm looking to perform a binary classification using random forests, but I do not quite understand how to minimize the entropy of the data / what tests I should run on the nodes to do so. I'm fairly ...
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0answers
5 views

Smoothing strategies for features assuming values from countably infinite domains

I am in the midst of programming a simple Naive Bayes classifier as an exercise. It is supposed to perform word-sense disambiguation on natural language phrases, e.g. predicting the correct meaning of ...
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0answers
13 views

Asking tweet classification

I want to ask you the process to classify the tweet data. Now, I am working to Twitter data but i have confuse how to classify the tweet data using Mallet Tool. Example; I have 200,000 tweets. The ...
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0answers
25 views

Sample Weights for classification using Gradient-Boosted trees?

How can "weights" be given to different samples according to their relative importance while using Gradient boosted decision trees for classification? How does the ...
2
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0answers
43 views

Bayes' classification in R

For a machine learning class I am taking, on our first homework assignment we are given the following problem that has me stuck: ...
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0answers
10 views

F1 score for biased binomial data

I am applying a Bayesian classifier and would like to find out the f1 score. I determined the TP, TN, FP, TP. Unfortunately I had to find out that in my cross-validation almost in all test scenarios ...
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1answer
54 views

Linear regression of 0/1 response (Fig. 2.1 of The elements of statistical learning)

In chapter 2 ESL book authors write: Let's look at example of linear model in a classification context They fit a simple linear model $g = 0.3290614 -0.0226360\cdot x_1 + 0.2495983 \cdot x_2 + e$, ...
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0answers
16 views

The ethics of using an optimal multiclass feature set for binary classification

I'm currently trying to find the best feature set/network architecture configuration for a binary classification problem, however to approach it via the usual means of building and testing does not ...
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2answers
41 views

Cost functions for weighting sensitivity and specificity in binary classification problem

I'm searching for a combination of sensitivity and specificity cost function because i want have more weight for sensitivity ( ...
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0answers
9 views

Feature selection based on cost function

Suppose that we are searching for best features using an optimization algorithm for a classification model (MLP,SNM,Regression,etc...). We should set a cost ...
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1answer
29 views

Classification tips for a begginer

I'm doing a graduation work that involves applying Classification algorithms in a dataset of matches from Dota 2 (a popular MOBA game). Here's an explanation of the problem: Dota 2 matches are played ...
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0answers
17 views

Logistic Regression with different priors

I am using standard logistic regression for classification with reasonable results. As expected I get a probability of 0.5 for query points "far away" from the data. However I would like to assign ...
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0answers
26 views

Optimize number of layers and neurons with an optimization algorithm

I have a neural network that i want optimize number of hidden layers and neurons in every layer using an optimization algorithm like ...
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1answer
23 views

Composition of bankruptcy probability and firm size

I'm using neural network for a binary classification problem of bankruptcy prediction using patternnet function in MATLAB, so i ...
1
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1answer
33 views

What is a good AUC for a precision-recall curve?

Because I have a very imbalanced dataset (9% positive outcomes), I decided a precision-recall curve was more appropriate than an ROC curve. I obtained the analogous summary measure of area under the ...
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0answers
14 views

Multi-class logarithmic loss function per class

In a multi-classification problem, we define the logarithmic loss function $F$ in terms of the logarithmic loss function per label $F_i$ as: $$ F = -\frac{1}{N}\sum_{i}^{N}\sum_{j}^{M}y_{ij} \cdot ...
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0answers
19 views

Stream classification of time series

I have a set of time series $\mathcal{Y}$, and a test time series $T$ for which I need to find the closest matching time series $Y_i \in \mathcal{Y}$. This has to be done online, i.e., $T$ is a stream ...
1
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1answer
45 views

Does Fisher linear discriminant analysis (LDA) require normal distribution of the data in each class?

Does Fisher linear discriminant analysis really require the data distribution in each category to be normal? I see two versions. The first one states that it requires the normal distribution and ...
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0answers
2 views

How can I counteract the effect of a degenerate classifier in an OVA Model?

Suppose I build a OVA classification model for classification with more than 2 possible classes (a model of sub-models, where each submodel predicts the probability of a data point belonging in a ...
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0answers
10 views

permutation test with a distribution of correct values

I want to test my classifier performance using a permutation test. I know how to test one actual value against a null distribution of values generated by randomly reassigning labels to my two classes. ...
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0answers
42 views

The best way to solve particular classification problem?

I got training set (time series) of size approximately 2 million precedents {x,y}. Each x is a vector of size 20 and each y is a binary vector of size 10 like {1,0,0,1,1,0,1,1,1,0}. For a new input x ...
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0answers
27 views

Order of preprocessing steps in a binary classification problem

I have these stages (ordered) for preprocessing in my binary classification problem. Dividing data based on criteria (class1 and class2 databases) Outlier ...
0
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1answer
31 views

Best algorithms for ordinal classification

I am working on a data set of about 34K rows trying to predict an ordinal response variable using R. I have tried association rules, random forest and ordinal regression. Does anyone have experience ...