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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10 views

Experiment design: classifying 3 classes (2 easy, 1 hard)

Bit of background: I have a problem of classification of 3 classes. Given the training set (80%) and a held-out set (20%), I found out that 2 classes are easy to discriminate/classify. The third class ...
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11 views

Neural Networks sigmoid activation with bias updates

I am trying to figure out if I am creating an artificial neural network using the sigmoid activation function and using bias correctly. I want one bias node to input to all hidden nodes with static ...
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6 views

Detecting false positives of a classification algorithm

I run a web service with a lot of users. Some of these users are involved in undesirable behavior (e.g. trolling). I've come up with a classification algorithm to detect these users (and deactivate ...
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19 views

Regression/classification, how to accommodate the missing columns of data?

I would like to apply any regression methods, such as the ones available using WEKA libraries (for example, SVMs, NNs, Random Trees,...) . However, I am getting very low results since I am missing the ...
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24 views

What is the next step after acquiring the parameters(means, covar, priors) from GMM via EM

I am comparing the results achieved from clustering via K-means and GMM. For comparison I have accumulated a dataset of images. The training set consists of 359 images. I used SIFT to extract the ...
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13 views

How to use Similarity Measure in K-nearest neighbor Classification?

I have a similarity measure just like cosine. How can i use that similarity measure in traditional k-nn classification? Please provide some literature review (research papers) details which i should ...
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1answer
30 views

Feature Selection: Information Gain VS Mutual Information

Setting: Multi-class classification problem with discrete nominal features. There are many references mentioning the use of IG(Information Gain) and ...
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2answers
32 views

Classes distribution in training set

In my actual data class A has 90%, class B has 9% and class C has 1% (numbers are made up for sake of simplicity). Now I want to prepare a training set for my classifier (I plan to use Vowpal Wabbit). ...
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11 views

unary classification in PyBrain

I've just started using PyBrain for some data classification work, and I've gotten it working pretty well where I have data from all possible classes and I can train the network using all the classes. ...
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2answers
37 views

Suitabiility of the confidence score generated by SVM as a proxy for membership function

SVMs can generate a confidence score which is basically like a probability for a particular data item to belong to the particular class. I want to use this probability as a proxy for the 'distance' of ...
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14 views

How to split a class which is not very cohesive?

Using the silhouette width metric I can find out as to how well each object lies within its class after classification is done. I next find the average silhouette width of objects within a class and ...
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6 views

Almost constant Target variable

I am building a classification model and my target which is dichotomous is close to 95% 0 and 5% 1. Is there any rule or guidelines/assumptions that I should follow for an almost constant target ...
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2answers
77 views

Semantics rules? A classification challenge

Suppose we make interviews on a large number of households in which we ask, among other things, the sex and age of the individuals living in the household, and also who among these individuals is the ...
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9 views

Analytical or explicit solution for optimal threshold of a simple classifier?

Given a training sample $\{(x_i, y_i): y_i \in \{-1,1\}, x_i \in \mathbb R, i=1,\dots, n\}$, construct a classifier $f_t, t \in \mathbb R,$ of the form $f_t(x) = 1$, if $x \geq t$, and $f(x)=-1$, if ...
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16 views

Combined Two CLassifiers

I am involved in a research where i need to classify group of words (strings) into two classes I am currently reached a dead point where my classifier is not doing as i expected. I used like three of ...
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3answers
82 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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15 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
15 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
25 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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1answer
96 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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3answers
123 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
11 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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22 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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18 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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20 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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7 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
59 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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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
32 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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21 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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1answer
14 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
22 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
11 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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20 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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86 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
7 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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11 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 ...
1
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1answer
26 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
41 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
14 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
22 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 ...
1
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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
15 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
32 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
45 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 ...
1
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1answer
62 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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18 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 ...