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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Time Efficient Estimation of A Single-Index Model (Klein & Spady estimator)

I am currently having an issue with time to estimation for a very large dataset using a semiparametric single index estimator (Klein & Spady). Specifically, I am attempting to estimate bandwidths ...
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12 views

fuzzy clustering and multi-label classification

I’m working on a clustering problem that I would like to extend to multi-label classification. Basically, I want to generate a number (x) of clusters using something like fuzzy c-means and using the ...
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14 views

Why is my DBN predict only 2 out of 5 classes?

I'm using the Deeplearning.net DBN tutorial to train my data set. I normalize the feature set to zero-mean-unit-variance. However, I can only get the network to predict 2 out 5 classes even though the ...
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20 views

Incorporating background knowledge into a model?

I have built a model that predicts the class of an observation, based on explanatory variables A; i.e. predicting P(y|A). From domain knowledge and academic literature, we know that other variables, ...
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15 views

Best practices to compute TFIDF matrix based on another TFIDF matrix in R

I'd like to compute a TFIDF matrix (tfidf_matrix_b) based on a previously computed TFIDF matrix (tfidf_matrix_a). Is there a ...
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1answer
39 views

Classification in real time without prior knowledge of the number of classes

Is there an implemented algorithm (with python/R or java in preference) that can classify incoming data from an unknown generator with absolutely no prior knowledge or assumption. For example: Let G ...
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17 views

Multinomial Logistic Question

Using PROC Logistic with the glogit link, I am attempting to classify records according to 1 of 3 responses (0, 1-2, 3+). After cleaning and running multiple models, I landed on what I thought was a ...
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2answers
77 views

Machine Learning Methods for Binary Classification

I was hoping to get a nice list of alternatives to logistic regression and decision trees for binary classification ("Yes vs. No" or "Cured vs. Not cured"). I am more interested in identifying the ...
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12 views

A Binary Classification to Distinguish two Different Models?

I have two functions, a step function $f(x)$ and an inverse exponential function $g(x)$. Together, they explain virtually all the data when combined as a piecewise function. Some of the data points ...
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7 views

Performance of Decision tree and Association rule mining with increasing size of training data

I planning to do misconfiguration identification using some available dataset. So I have two dataset, one with enough number of observations, say from 1000 to 3000 and another one with less than 50 ...
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34 views

Sparse features and dimension reduction

Let sparse feature be a feature which values are subsets of some set. For example, the set of countries from which user logged to server is a sparse feature, because for each user we've got the set of ...
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0answers
9 views

Standards for classifying dependent variables?

So I am working with the UCI Machine Learning Repository on Crime vs Community to practice my machine learning, and statistics. The data gives the crime rates of the areas but it is up to me to ...
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14 views

SVM always predicts same label

I have 11 labels. I trained an SVM model: ...
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15 views

Class imbalance problem - Random under sampling

I have a class imbalance problem in my data set (99.5%,0.5%). I understand that there are various sophisticated ways to deal with this problem, but I am trying a fairly simple approach - Random under ...
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9 views

Interpreting the lift curve

Suppose we have two classes: A and B. Suppose we use a logistic regression to assign each unit to A or B. The curve lift is calculated through this formula: $\frac{n_{22}/n_{.2}}{n_{2.}/n_{..}}$ ...
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1answer
30 views

Statistical test for classification models

I have 3 models from which, for each model, I train a classifier and then evaluate it, currently using stratified 10-fold cross validation and then take the mean accuracy ratio of these from each ...
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1answer
18 views

Pruning Conditional Inference Trees

I am trying to build a prediction model using classification trees. While I tried the "rpart" package, the results were not entirely satisfactory. Hence, I thought of exploring conditional inference ...
2
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0answers
30 views

Is it possible to explain the performance of a classifier by the specific properties of a data set?

I receive the following comment from a reviewer : "I think that the authors could explain in more details the results. For instance, there is no discussion linking the specific properties of the ...
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1answer
40 views

Is building training data set from unlabeled data considered as a scientific contribution? [closed]

Is building a training data set, from unlabeled data, for a machine learning classifier considered as a scientific contribution?
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0answers
17 views

Classifying colors based on prior knowledge of what the colors will be

I'm trying to think of a way to classify a set of observed pixel values based on prior knowledge. I'm projecting an image with a set of known colored ordered vertical stripes (say a red stripe, a ...
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1answer
13 views

Clarification on using train vs glm vs rpart for classification problems in R

I am using the glm function in R to perform logistic regression. I converted the outcome variable to a numeric between 0 <=y <= 1 as follows ...
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1answer
131 views

Bag-of-Words for Text Classification: Why not just use word frequencies instead of TFIDF?

A common approach to text classification is to train a classifier off of a 'bag-of-words'. The user takes the text to be classified and counts the frequencies of the words in each object, followed by ...
2
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0answers
25 views

Class probabilities in Neural networks

I use the caret package with multi-layer perception. My dataset consists of a labelled output value, which can be either A,B or C. The input vector consists of 4 ...
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2answers
52 views

Odds vs probability in logistic regression

I am going through Trevor Hastie's Classification Techniques. Its says Odds are traditionally used instead of probabilities in horse-racing. I still don't understand how they relate more ...
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17 views

Describing the distribution of N points in D-dimensional space?

I want to tackle a classification problem by describing the samples as its descriptors' distributions. So let's say each sample has a label, and $N$ vectors of dimension $D$, (N and D are fixed) and ...
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12 views

3D Zernike moments vs. Spherical Harmonics. Which one has higher discriminative power as shape descriptor?

I am looking for a comprehensive study that has performed comparison of different 3D shape descriptors for classification/clustering problems. Particularly, I am interested in 3D Zernike moments vs. ...
4
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1answer
87 views

Incorporate new unlabeled data into classifier trained on a small set of labeled data

I have a set of 400 labeled samples (8 numeric features) on which I trained a binary classifier. The problem I am facing is that once the classifier is shipped to the users, I will get additional ...
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0answers
29 views

Which unsupervised learning method should I use on classification on many point cloud datasets?

I have a few abstract and high dimensional point clouds in the form of distance matrices. I want to do unsupervised learning on this dataset. The problem is, I am not using one distance matrix, but ...
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0answers
17 views

Integrated Classification Likelihood computation for R package HDclassif

I'm in the process of fitting some mixture models to some data I have. As this data is high-dimensional, I used the subspace clustering package HDclassif. As the package has no option for the Akaike ...
5
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1answer
79 views

Where must we use Bagging or Boosting?

I want to know when Bagging is better than Boosting? How I select appropriate method for my classification task? I think when we have many outliers in our data-set, Bagging must be better than ...
0
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1answer
23 views

What is the best measure for unbalanced multi-class classification problem?

What are some possible classification metric for an unbalanced problem ? Due to skeweness of the distribution, accuracy value is not so meaningful. For instance, if I predict all the classes to class ...
2
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1answer
79 views

How can using Logistic Regression without regularization be better?

I'm using this Java machine learning library: https://sites.google.com/site/qianmingjie/home/toolkits/laml From the library I'm using Logistic Regression: ...
1
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1answer
48 views

Can we express logistic loss minimization as a maximum likelihood problem?

I have a simple question about the equivalence of loss minimization and likelihood maximization for logistic regression. Say are given some data $(x_i,y_i) \text{ for } ~i = 1,\ldots,N$ where $x_i ...
0
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1answer
9 views

GPML producing wrong output using correct target labels

I am using the GPML code found here. The key function in the aforementioned library is the gp function described below: Two modes are possible: training or ...
0
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0answers
8 views

Is there any lower limit for number of positives when generating lift plot?

I am wondering if there is any condition on number of positives in test set when I am trying to compute lift plot to check the properties of my classifier?
2
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0answers
52 views

Isn't caret SVM classification wrong when class probabilities are included?

*Please note this question is about the Platt probabilistic output and SVM class assignment, not about the code or the package itself. It just happens to be the code where I stumbled on the issue. In ...
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2answers
37 views

Binary classification when one class consists of multiple subclasses

I have the situation where I want to distinguish between two classes $C_1$ and $C_2$, where $C_2$ consists of three different types of subclasses $C_{2,1}$, $C_{2,2}$ and $C_{2,3}$. Also, it is easy ...
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0answers
9 views

declare class label in classifier with only X field in r

I have a simple problem: I can do SVM classification with some packages but have problem with others. let's say: my data set for training= ds and for testing ...
2
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3answers
66 views

Criteria for classification performance

In binary classification, are there criteria or guidelines available to judge if classification performance of the testset (unseen data) is poor, medium or high? I realise that this may depend on ...
3
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3answers
365 views

KNN: 1-nearest neighbor

My question is about the 1-nearest neighbor classifier and is about a statement made in the excellent book The Elements of Statistical Learning, by Hastie, Tibshirani and Friedman. The statement is ...
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0answers
40 views

How to make correlated variables, uncorrelated?

I have 7 independent variables with 3 observations and they are highly(<95) correlated with each other (each of them) and my dependent variable is head count for 3 years( thus only 3 observations ) ...
5
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1answer
182 views

Is it feasible to transform each variable differently while doing multiple regression

I have a dataset with 10 variables ...is it feasible to transform each variable differently while doing multiple regression... for example new_V1 = log(v1) New_V2= V2^2 New_V3= 1/V3 Likewise ...
1
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1answer
22 views

How to classify samples with different features?

Assuming we are considering following classification problem: We have a dataset containing the time when a user call a taxi in one day, but different users call the taxi different times. For example: ...
0
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1answer
11 views

Classifier for weighted class label

Is there any rule-based classifier which be able to classify samples with weighted class labels. In other word, different confidence in tagging samples. My problem deals with learning samples from ...
0
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0answers
8 views

Supervised Classification of one element in each Set

Suppose I have the following supervised classification problem. Given some list of sets X, where $$ X[i] = S^{(i)} $$ and $$ S^{(i)} = \{X^{(i)}_1, \ldots X^{(i)}_{L^{(i)}}\} \qquad \text{where } ...
0
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2answers
46 views

Using k-means for reducing the size of the training set of a Kernel SVM

I have a classification problem with the following characteristics: a few million data points around one hundred features non-linearly separable Training a SVM with an RBF Kernel is not feasible ...
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0answers
38 views

Which algorithm fits best to a transactional data classification problem?

I have transactional database - the data looks as follows: ID - COLUMN1 - COLUMN2 - COLUMN3 0 - A - B - C 0 - A - D - C 0 - E - B - C 1 - A - B - C 1 - A - B - C 2 - ...
3
votes
1answer
107 views

How to reduce number of false positives?

I'm trying to solve task called pedestrian detection and I train binary clasifer on two categories positives - people, negatives - background. I have dataset: number of positives= 3752 number of ...
2
votes
0answers
28 views

Anomaly Analysis (K-Means) - finding suspicious activities/operators

I am relativly new to the field of data mining and want to make a anomaly detection on transactional retail data. I want to use a simple anomaly detection (kmeans at the moment) for finding suspicious ...
1
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20 views

What is cross classification?

I could not find a Wikipedia page, can someone explain to a non-statistician what cross classification is? An example where this technique is used in financial risk assessment (credit risk / market ...