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 ...

learn more… | top users | synonyms

1
vote
1answer
34 views

Classification score for Random Forest

I'm learning about the Decision Tree and Random Forests. But there is something I don't really understand. I have a training set and a cross-validation set. I need to train different Random Forests, ...
1
vote
0answers
38 views

Cross-Validation: how to choose k for small datasets (n=900)?

In a binary classification task, I have a small training set (n=900, 9 features). The two groups are not symmetric (1 = 560, 0 = 340). I also have a test set (n=400) where I don't know the class ...
1
vote
0answers
10 views

Distance measure for categorical attributes for k-Nearest Neighbor

For my class project, I am working on the Kaggle competition - Don't get kicked The project is to classify test data as good/bad buy for cars. There are 34 features and the data is highly skewed. I ...
1
vote
0answers
24 views

How to prepare a dataset for text classification

I would like to compare some algorithms for performing sentiment classification (Naive Bayes, SVM, and ...
0
votes
1answer
21 views

Chi-squared Vs Mutual information

Is chi-squared feature selection better than Mutual information based feature selection mechanism?
0
votes
0answers
20 views

Using Naive Bayes as vectorizer and SVM for classification in Java [on hold]

0 down vote favorite I am trying to classify legal case documents which are in text format, in different folders like Civil, Land, Criminal, e.t.c, I intended using Naive Bayes as Vectoriser to get ...
0
votes
0answers
8 views

assign asymmetric cost in SVM classification in R?

I know that we can set a symmetric cost in R for svm,how do we set an asymmetric cost? I want to do a grid optimization like the following: ...
1
vote
0answers
10 views

DNA sequence classification

I have a database of 3190 instances of DNA consisting of 60 sequential DNA nucleotide positions classified according to 3 types: EI, IE, Other. I want to formulate a supervised classifier. At the ...
0
votes
0answers
10 views

R: “weights” option will help calibrating class inequality?

I have a database with a binary response variable and 100 predictors (correlated and uncorrelated). I want to try the machine learning techniques in R I've been reading about in the last 3 weeks ...
0
votes
0answers
9 views

Find input image (ID,passport) in imagesDB based on similarity

I would like to decide if image is exists on DB images (pictures of IDs,passport,Stu. card,etc) I thought of KNN alghorithem that will plot the K closest images. Options for distance metric: 1) sum ...
2
votes
0answers
12 views

How to measure how 'well' I am matching Google keywords?

For google keywords you can bid on a broad match. For example let's say I bid on the keyword 'best hamburger' and somebody searches 'What sort of beef makes the best hamburger?' and 'eat best ...
0
votes
1answer
30 views

SVM parameters clarification

James et al. in An introduction to the statistical learning (p. 351) claim that the solution to the support vector classifier problem involves only the inner products of the observations. They ...
0
votes
1answer
25 views

Effect of combining features on classification

I have 2 string features F1 and F2 based on which I am trying to perform classification. I have two choices, either to use the ...
0
votes
0answers
21 views

How to analyse a factor experiment with feature extraction, clustering and classification algorithms as factors?

Currently I am doing my final project, which consists of designing an experiment to test several combinations of algorithms on a dataset, such as feature extraction, clustering, classifiers and ...
0
votes
0answers
8 views

Synthetic Minority Oversampling with Binary Features in the data

I am planning to use SMOTE or ADASYN for creating synthetic observations for a classification problem as the data is imbalanced. The question is, there are Binary variables in the Feature set, and I ...
0
votes
1answer
30 views

How many features can we use to avoid overfitting the classification?

We have a classification problem: classify type A tumour from type B tumour. In total we have 50 patient cases (25 A and 25 B cases). We use texture or shape analysis to generate features we can ...
0
votes
0answers
9 views

glmnet: which is the reference category or class in multinomial regression?

Following post Why {glmnet} can be calculated parameters for all category? I have 4 categories or classes or responses for y (thus multinomial): cat1, cat2, cat3 and finally no-cat. I want my "no ...
1
vote
0answers
12 views

Data At Varying Granularity

I'm sorry for asking such a simple question, but for some reason it is throwing me off. By "granularity" I mean level of the data. For example, say in the classic example of spam classification you ...
0
votes
1answer
7 views

Binary Classification vs. Aggregating Multi-Class Predictions

Sorry for the convoluted question, I'm sure there is a better way to phrase this. Basically, say I have a 5-class target vector, but I'm only interested in membership in a subset of the classes. For ...
1
vote
2answers
49 views

Multi-class Classification using SVM with PCA

I'm doing an image classification task and the number of features of each example image is pretty huge (3,072: # pixels in each image). I'm thinking of using PCA to reduce the # features of each image ...
1
vote
1answer
39 views

Combining pca and classification algorithms

For some classification algorithms, assuming independence of data helps reduce the number of parameters to estimate. Why then not just to apply a method like pca or ica to the original features to get ...
1
vote
2answers
29 views

Choosing the number of principal components to retain before training a neural network for classification

I am working on neural networks and I am currently creating a perceptron that will work as a classifier for a data set of images with faces. I am required to perform pca (principal component analysis) ...
2
votes
0answers
14 views

Baseline for Precision-Related Metrics

When working with ROC-AUC as a metric for binary classification, one often considers a value of 0.5 as a baseline from a random classifier (i.e. a data-blind classifier that randomly classifies test ...
0
votes
0answers
22 views

Finding genuine arrears and default arrears from rent payment patterns

I am currently working on some housing data - in particular analyzing the tenants' rent payment information and I am stuck on progressing with the following: I have to classify tenants based on their ...
0
votes
0answers
30 views

Trying to find a classifier that will give me probability predictions between 0-1 in weka

This is the first time I've done any sort of predictive modelling and I think I've really confused myself. I have a training set of data with a column at the end that has either a 1 or a 0 in it. ...
0
votes
1answer
20 views

Structural risk minimization and SVMs

I know what is SRM but I didn't understand the relation between SRM and SVMs. Can anyone explain me this? Why they say that SVMs rely on a SRM approach? Thank you so much!
0
votes
0answers
9 views

Is it OK to resample data in one vs all multiclass classification?

I have a multiclass classification problem and decide to use one vs all logistic regression. Since some classes are very rare (pos vs neg is like 1:100), I plan to use some balancing strategy during ...
5
votes
1answer
40 views

Is it possible to have a case where $D'$ is zero but Logistic Regression is still able to classify accurately?

I want to know if it is possible to construct a problem with following properties: $M_1$ is $n \times p$ matrix of $n$ observations from Class A $M_2$ is $n \times p$ matrix of $n$ observations from ...
1
vote
1answer
16 views

How to get random classification to assess the performance of classifier with McNemar test?

I'm trying to replicate a study where the author used the McNemar test to assess the performance of classification compared to random classification. I have the original classifier and I'm using R to ...
0
votes
0answers
21 views

Appropriate classification model for combination of continuous, binary and categorical inputs

I have a binary classification problem for classify my samples to two classes (class_1 and class_2). I have different kinds of ...
1
vote
0answers
25 views

Intution on Interchangability of Regression and Classification

Dear Oracles of CrossValidated, I've been trying to gather intuition on the relationship between methods that seems to be escaping me. Can someone explain how regression and classification can be ...
1
vote
2answers
44 views

Why Adaboost with Decision Trees?

I've been reading a bit on boosting algorithms for classification tasks and Adaboost in particular. I understand that the purpose of Adaboost is to take several "weak learners" and, through a set of ...
1
vote
1answer
40 views

Testing Logistic Regression Classifier in R

I am testing the logistic regression classifier in R. I created some test data like this: x=runif(10000) y=runif(10000) df=data.frame(x,y,as.factor(x-y>0)) ...
1
vote
0answers
13 views

Using Canonical Correlation Analysis (instead of EFA/PCA) to reduce the dimensionality of two sets of variables prior to clustering/classification

I have two sets of paired continuous data obtained from two tests. My goal is to answer the following research questions: Q1. To what extent can results on one test be used to predict the results on ...
0
votes
0answers
7 views

How to deal with clustered features in classification

Imagine there are three classes of data, labeled A,B and C. I have separated the train set ...
0
votes
1answer
17 views

ROC / AUC for polynominal Labels

How can I calculate the Area Under Curve for a classifier of a plynominal label in Rapidminer? I could only find a performance operator for binominal labels that provides the AUC value.
0
votes
0answers
16 views

Ideas to identify cutoff points for tumor classification?

My lab models breast cancer in mice. I am using a 36-gene signature (derived from one of our mouse models). In the signature, all genes are elevated. I have 997 human samples and would like to apply ...
0
votes
0answers
15 views

Determine performance in which subject improves overall performance

I have a dataset in .csv format as shown: ...
1
vote
1answer
44 views

Ratio between positive and negative examples in a training problem

When training a 0/1 classifier, what should be the ratio of positive to negative, how to decide the ratio between them based on the classifier I use and the data set under analysis?
0
votes
0answers
13 views

Is the approach for PLSDA for categorical variables the same as that used for “PLS for regression”?

I understand the approach used for partial least squares for regression (PLS) where the principal components are chosen such that the correlation between the scores in the principal component space ...
0
votes
0answers
36 views

Learning Decision Trees on Test Data Using R

How can I use R to learn classes on test data? I currently have a training set of about 1000 entries and a test set of about 10000 entries. I split it up so that the training set has the class label ...
0
votes
0answers
33 views

How do we generate the ROC curve for Linear Discriminant Analysis method

I know the method to generate the ROC curve for other methods such as naive Bayes where the tuning parameter is the threshold like also in logistic regression. If we want to generate the ROC curve ...
0
votes
1answer
16 views

What is the intuition behind the Kappa statistical value in classification

I understand the formula behind the Kappa statistic value and how to calculate the O and E value from a confusion matrix. My question is what is the intuition behind this measure? Why does it work so ...
0
votes
0answers
6 views

Classification under uncertainty in observations

I am tackling a multiclass classification problem where the values of the independent variables are not known with certainty. Instead, each observation is represented by a multivariate Gaussian pdf ...
1
vote
1answer
13 views

Extract features to explain different states of the world

I have a problem that can be seen as the inverse of a classification problem. I don't need to classify points, but to explain the differences (if any) between points in different, pre-specified ...
0
votes
0answers
10 views

Do you know about SVM plait?

I need to know about how I can applied many single SVMs? because I have read about SVM plait that does this kind of classifications that is using many single SVMs to improve the classification process ...
0
votes
0answers
14 views

Can you use accelerometer data for classification with Conditional Random Fields?

I want to recognize activities, based on accelerometer data from the smartphone. I studied Conditional Random Fields and the CRFSuite. Now I am Confused. In my opinion CRF training uses static single ...
0
votes
0answers
19 views

Statistical test for Comparing 6 classifiers over 5 datasets

I did the Friedman test to compare 6 classifiers which are tested over 5 datasets. The null hypothesis was rejected so I proceed with the post-hoc Bonferroni. The X classifier is always first on all ...
0
votes
0answers
5 views

Using Quality metrics of BIRCH Clusters

What is significance of quality metrics of BIRCH Clusters Distance3 and Distance4. Appreciate if there are pointers are how to use Average Intra Cluster Distance (D3) and Average Inter Cluster ...
0
votes
0answers
9 views

Testing Cluster Assignment/Pattern Matching against BIRCH Clusters

I have a dataset of size >35K in size / >50 dimensions. Used BIRCH algorithm for clustering. While testing, the data points with which cluster formed is not matching i.e., The data point shows closer ...