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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12 views

Model comparison between glm (with Firth correction), random Forest, penalised SVM

I am currently developing three models to classify features of gene sites. I was using glm (with Firth correction), random Forest and SVM to build the models and I used forward and backward ...
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2answers
22 views

Feature selection before neural network classification

I have a training set of 87 samples and 9480 variables. My predictors are continuous and my response variable is binary. I'd like to use the caret package in R to tune a neural network classification ...
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8 views

classification when some classes can be grouped

Is there a systematic way to address a classification problem where some classes are dependent and thus can be clustered to construct a larger superclass? I gave an example in this Classification ...
4
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2answers
62 views

Classifier with adjustable precision vs recall

I am working on a binary classification problem where it is much more important to not have false positives; quite a lot of false negatives is ok. I have used a bunch of classifiers in sklearn for ...
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20 views

How to categorize users based on their movie views?

Apologies for cross posting. I have a dataset of size (61573, 25). The rows represent users whereas the columns represent ...
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0answers
18 views

Hidden Markov Model For Text Classification

I have a question about HMMs being used to classify an entire text body under examination. This is as opposed to classifying a subset of a text body under examination. For example, classifying a news ...
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1answer
32 views

Classification when some classes are dependent

I think my problem can easier be explained via an example: Assume we have a dataset containing the images of 10 different mammals, let's say lion, elephant, cat, ... and horse. We have a 20-class ...
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7 views

What is an “example based” metric?

In the context of multilabel learning I came across several "example based" metrics, for instance example based Recall, example based Precision etc. (see here) I do know the concept of Recall etc. ...
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0answers
15 views

Looking for Approach: Cost-sensitive analysis based on probabilities and costs that differ per example

I've been struggling with the following problem. I have a logistic regression model that returns probabilities for three classes (A, B, C). The problem is, I need to choose A, B, C or refrain from ...
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18 views

How the scores are assigned while plotting K-S Chart? [on hold]

There is the a tutorial on web here (about halfway down the page) on how to use a KS-chart to evaluate a classifier. Here is the chart that illustrates it: How does the model assign a score ...
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48 views

Kolmogorov-Smirnov(KS) statistic for multi class problems [on hold]

How to evaluate the performance of multi-class classification model using KS statistic? My goal is to evaluate the classification algorithms namely Random Forest,Logistic Regression and SVC using ...
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0answers
15 views

Which classifier to be used in a recommendation problem?

I am supposed to deal with a recommendation problem in which the input vectors of 1 million bits represents the interest of 1 million people in a special product and the output would be the a binary ...
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20 views

RandomForest Classifier With Very High Success Rate

I'm having a weird problem that may suprise you all. My classification rate is too high on my test set. I'm using scikit-learn packages, and I'm very suspicious of these classification rates, as they ...
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0answers
12 views

should we check for multicollinearity when building a discrimination analysis?

In fact, I have two questions: 1- do we need to check for the multicollinearity when we deal with linear discrimination analysis? 2- how to solve those variables which are multi-collinear? Lets ...
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1answer
15 views

How to weight data set for training?

I'm trying to build a training set for a classifier. A vector evaluates to either conclusive 'C' or unconclusive 'U'. ...
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0answers
7 views

Text classification: Dealing with potentially false classified instances

My goal is to complete a supervised text classification task with R. There are several classes, of which some of the class counts will be relevant for a subsequent analysis. I have already tried a few ...
1
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0answers
32 views

what is the differences between LDA and MLR? [duplicate]

I know that Linear Discrimination Analysis (LDA) is used for classification and Multiple Linear Regression (MLR) is for regression. Lets say I have a matrix X (independent variables) and Y(dependent ...
0
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1answer
34 views

Model Selection and RFE using caret

I'm faced with a high dimensional (samples=148, features=20000), supervised binary classification problem. Which I would like to approach with an ensemble of classifiers, that will classify using a ...
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0answers
33 views

compare muliptle probabilities

I have built three decision tree model to predict the response for three offers (One model per offer). I want to find the best offer for each customer based on the predicted probabilities. All offers ...
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0answers
20 views

Cross-validation for parameter tuning in data mining process (KDD)

In my project I want to compare different classification algorithms to solve a specific problem with a specific dataset. To do this, I divided the dataset in 2 parts. With the first (bigger) part I ...
2
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2answers
36 views

Sensitivity, Specificity and Misclassification rate

I stumble upon a problem that I'm sure someone else already had. Assume you have: A cohort, composed of 10 healthy subjects and 100 diseased subjects. A model M such that M(patient) = diseased. ...
3
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0answers
57 views
+50

Missing values with Community structure in networks?

Is there a way to predict Missing values with Community structure in networks? I have a data set with a couple dozen variables, such as age, level of education, self-assessed (via a Likert scale) ...
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0answers
26 views

what's the best empirical macro/micro F1 score?

Theoretically it should be 1. In the following presentation it's said that "0.5 to 0.55 (micro) F1 score is best for multilabel classification problems" I tried to investigate this statement but ...
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0answers
31 views

How do I classify data with multiple dimensions using a gaussian classifier? [closed]

I've computed the equation inside the brackets (but not i): Features=dimensions (x,y)..R^n Ck being the covariance matrix, z being the input vector, u being the mean vector, N being the number of ...
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0answers
15 views

Does centering or mean normalizaiton alone every help in feature scaling?

In feature scaling, one way is to subtract the mean (centering) and then divide by the standard deviation for all data points. Suppose we just centered the data and didn't divide by the standard ...
4
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143 views

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 ...
1
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1answer
35 views

How does data standardization affect a classifier?

How does standardization of data (subtracting the mean, dividing by standard deviation) affect classifiers? Namely, how (if at all) do different types of classifiers get affected by such an operation? ...
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16 views

Is this a correct way to do document classification using topic modeling?

I am using LDA to extract topics. I want to do topic modelling and use the topics as features to do document classification. I am proposing the below approaches using scikit-learn. I want to know ...
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1answer
14 views

Any way to exploit relations between examples in dataset?

Suppose I have a dataset with k examples: id1, feature1, feature2 .. featuren ... idk, feature1, feature2 .. featuren For which I cat mark a training set and feed ...
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0answers
30 views

Finding score of aspects

From online customer reviews, I have extracted aspects(nouns). I have sentiment lexicons(list of positive,negative words). Example ...
0
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1answer
22 views

Feature normalization in Text Classification

I'm doing Text Classification in R, and my initial features are just word frequency inside a Document. For example: ...
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0answers
23 views

Classification Algorithm For Small Sample Sizes

I am looking at a problem now where I need to train a classification algorithm. There are only 2 classes, lets call them A and B, and I want a value between zero and one indicating the probability ...
0
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0answers
16 views

Mean Average Precision in Matlab with liblinear and vlfeat

I want to find the mean average precision (meanAP) from a classification problem. I am using liblinear for classification and I am trying to use vlfeat for the precision because it already includes a ...
0
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1answer
88 views

From the Perceptron rule to Gradient Descent: How are Perceptrons with a sigmoid activation function different from Logistic Regression?

Essentially, my question is that in multilayer Perceptrons, perceptrons are used with a sigmoid activation function. So that in the update rule $\hat{y}$ is calculated as $$\hat{y} = ...
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0answers
9 views

Need to order a set of colors to build a feature vector

I am working on Computer Vision task of object classification with python and OpenCV. Currently I am extracting some characteristic colors of an image using K-means clustering on all the pixel to ...
3
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1answer
256 views

Why doesn't a non-linear kernel improve accuracy in high dimensions compared to a linear kernel?

I read somewhere that if the number of dimensions in your feature set is very high, then a non-linear kernel such as RBF (or any other) may not help in increasing accuracy compared to a linear kernel. ...
0
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1answer
39 views

neural network high misclassification rate

I have to create a binary classifier for a dataset of approx 150 samples and 50 binary features. The classifier has to be a feedforward neural network (NN). My problem is that while the NN performs ...
0
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1answer
17 views

Which one to choose and when? One-v/s-One and One-v/s-All classification for multi-class classification

In case of multi class classification task, how do we decide which among the two options viz. one-v/s-all and one-v/s-one do we choose for model building? Is there some criterion based on which we ...
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0answers
14 views

CHAID tree analysis in SPSS

I am trying to build a decision tree using CHAID in SPSS. I am however getting only one node. I have tried taking several data inputs. I also tried out CRT. Is there some setting that needs to be done ...
0
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1answer
23 views

NLP: How to do feature normalisation for gender classificiation?

NOTE Before I begin, this F-measure is not related to precision and recall, and its title and definition is taken from this paper. I have a feature known as the F-measure, which is used to measure ...
0
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1answer
20 views

Cross-validation ($3$-fold) for optimizing ($C$, $\gamma$) in RBF-SVM

Let $\mathcal{X}$ be a training set which will feed a binary SVM with RBF kernel. $\mathcal{X}$ consists of $10$ positive examples and $100$ negative examples. I am interested in optimizing the ...
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0answers
12 views

What is the VC dimension of the conjunction of two linear predictors (space of points in the plane)?

I am heavily confused about how to calculate the VC dimension for the following problem: Considering the space of points in the plane, what is the VC dimension of the class of hypotheses defined by ...
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0answers
12 views

A binomial bandit v. a Gaussian process optimizer

I am reading about multi-armed bandits for web optimization, and I have come across a couple of options, two of which are the binomial bandit and the Gaussian process (an implementation here). Am I ...
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0answers
11 views

Picking a right classification metric

I have a classification problem that I ended up with only true positives (TP). Now, I would like to measure my classification but I wonder what kind of metric I should pick for the evaluation. Let ...
1
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0answers
22 views

How to select observation window and performance window for churn prediction?

I have to built a customer churn model for of a teleco. The churn rate is 15 %. There is no particular campaign conducted because the dedicated sales reps go and try to retain customers whenever they ...
1
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1answer
20 views

using unlabaled data in a classification problem (There is labeled data but it comes from a biased sample)

I have a binary classification problem. The task is to rank instances from high probability of fraud to low probability of fraud. The following data is available: ~7.000 instances of 0/1 labeled ...
0
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2answers
23 views

Classification of corpus into classes with imbalanced datasets

i am trying to classify some images in classes using the convolutional networks approach. However there are varying numbers of training examples per class. I am worried that that might cause ...
1
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0answers
35 views

A bunch of different types of variables (their combination also important) explaining one variable - which method?

I have a dependent variable - how much land does a household cultivate out of total in their possession. The answers are categorized in 3 different groups (1 - 70% - 100%, 2 - 40 - 70%, 3 - less than ...
0
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1answer
34 views

The performance of the classifier went bad after adding extra two features

I'm solving a classification problem using 10 features and logistic regression. The performance of the classifier is fine when I use the 10 features only, however when adding another 2 features, the ...
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0answers
10 views

How can I calculate AUC using Gini coefficient?

In the Gini Coefficient's Wikipedia page, it is defined as $G= 1 - \frac{\Sigma_{i=1}^n f(y_i)(S_{i-1}+S_i)}{S_n}$ for discrete variables, where $S_i= \Sigma_{j=1}^i f(y_i)y_i$ and $S_0=0$ ($y$ being ...