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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Compute the probability that the provided classifier label is correct

A binary SVM classifier provides a label $y_c^{(i)}$ for each $i$-th sample provided. This is not assured to be corresponding to its true label $y^{(i)}$, since the classifier could have computed a ...
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0answers
10 views

How to visualise the uncertainty of the classification?

I used SVM to do some classification, and SVM can output some probabilities (likelihood) value measuring how likely each data to be one particular class. For example, Data point 1: 90% (class 1) 5% ...
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0answers
9 views

learning if instances from a dataset are part of the same subset

I was wondering if there are some well-known machine learning methodologies for subset learning. In other words, to learn if two instances are part of the same subset or not (boolean label?). One ...
2
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0answers
19 views

How to use prior information about likelihood of related observations in classifier?

I am building a classifier about a certain kind of observation (in this case I'm using SVM but the question can be applied to any other classifier). My observations occur in related pairs, where the ...
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0answers
19 views

Support Vector Machine image classification in R

I'm looking for some direction for creating/running a support vector machine (SVM) classification on a multi-band Landsat image in R. What I have: Landsat 8 image with 8 bands plus a NDVI, and ...
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0answers
11 views

Compare optimization algorithms with stop criteria

I'm comparing Harmony Search and Genetic Algorithm for a specific finance case study. I set a early stopping criteria. If we don't have improvement in 1/5 of total iterations of algorithm, It will ...
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1answer
31 views

Log likelihood function for binary classification

I need help with this following task. There is a binary classification problem where each observation xn is belong to one of two classes (t = 0 and t = 1). The training data points are sometimes ...
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2answers
33 views
+50

Why do one-versus-all multi class SVMs need to be calibrated?

On the wiki page for multi-class support vector machines (https://en.wikipedia.org/wiki/Support_vector_machine#Multiclass_SVM) it states that "it is important that the output functions be calibrated ...
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0answers
27 views

High dimension Categorical Decision trees, Python? [on hold]

I'm just getting into the whole machine learning thing after much reading.. I'm trying out This contest on Women's health http://www.drivendata.org/competitions/6/ There are about 1,000 columns of ...
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1answer
43 views

Equivalent of AUC (area under the ROC curve) for two variables

I was wondering if there is a way to compute AUC using two variables instead of one as predictors. I got two populations after a follow-up, divided in Cases and Controls according to whether they had ...
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0answers
36 views

Combining bootstrap and cross validation

I recently read this paper: Estimating misclassification error with small samples via bootstrap cross-validation, by Fu et al. (BMC Bioinformatics, 2005). The authors talk about combining cross ...
3
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1answer
72 views

Two broad categories of dimensionality reduction

As a starter in dimensionality reduction (DR), I recently acquired the following understanding. There are two very broad categories of DR techniques: We can compute an analytic form of mapping from ...
2
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0answers
47 views

Combining two probabilistic predictions

I am solving a machine learning task in which I need to predict a label $\tau$ from input $\vec x$. The input $\vec x$ can be considered as two parts $\vec u$ and $\vec v$ ($\vec x$ can be thought of ...
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1answer
17 views

Asymmetric distance measure in k-NN classifier?

What is the problem with an asymmetric distance measure in k-NN classifier? I think it will not cause problem, so long as I compute the distance consistently, say always from ...
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0answers
6 views

Naive Bayes in text classification - Only classifying “0”

I have a data set of 1000 Amazon "art" category reviews. I want to classify Positive +1, Negative -1, Neutral 0 Ratings using user reviews. The final Naive Bayes classifier only predicts 0 for all ...
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0answers
17 views

Balancing Per-Class Accuracy of Multiclass Classifier

Suppose I have a multi-class classifier like Naive Bayes, k-Nearest Neighbors, Decision Trees, Random Forest, etc. The classifier maps a feature vector to (let's say) 3 classes: A, B, or C. My ...
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0answers
26 views

k-NN classifier with a data-dependent distant measure?

As we know, in a $k$-NN classifier, we have to define a distance measure. Imagine a case where I use a certain dimensionality reduction technique to project my high-dimensional data to 2D, and then I ...
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1answer
12 views

How can I implement probability prediction for One vs One classifier specifically in Sklearn?

I am trying to get probability instead of hard prediction by a One vs One classifier. It is not supported by Sklearn implicitly. Is there nay way to implement it by myself? If so please explain? For ...
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1answer
24 views

Prediction vs. Classification in neural networks

I am learning the backpropagtion algorithm, and would like to clarify some concepts. Suppose my training data set consists of 20-dimensional bit strings that are classified into 5 different classes. ...
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2answers
45 views

Kernel PCA and classification

I need to perform kernel PCA on the colon-­‐cancer dataset and then I need to plot number of principal components vs classification accuracy with PCA data. For the first part I am ...
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1answer
25 views

What are the techniques to deal with classifying sparse categorical features?

Suppose I have a group of features each one is sparse with a few number of values (1-10) what are the required preprocessing steps required to avoid degradation of the performance of the classifier ...
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3answers
34 views

Comparing SVM Model

Pardon my understanding of SVMs as it is very little. We often hear of ensemble classifiers and stuff like this. Say if i were to have 3 different SVM Models for the same dataset predicting a ...
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0answers
11 views

Binomial Distribution statistics quest [closed]

The Mountain States Office of State Farm Insurance Company reports that approximately 70% of all automobile damage liability claims were made by people under 25 years of age. A random sample of seven ...
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0answers
10 views

Learning spatial behaviour [closed]

I have a dataset consisting of several hundred trials of an experiment. During each experiment, I track the motion of thousands of objects through 2d space over some period of time. Some objects ...
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1answer
29 views

SVM Three Way Classification

I would like to verify the following methodology for using SVMs for three way classification. That is, the response $Y$ can be either $\{-1, 0, 1\}$: First train an SVM to distinguish between ...
3
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1answer
26 views

Cumulative match score

I have seen loads of graphs in papers of cumulative match scoring, but I can't find any information about what it means, or how it is created. A context that would be useful to see the explanation ...
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1answer
25 views

likelihood of string of events given a string of probabilities

There are two classes of strings of events. E.g. A: 0,0,1,2,2,3,4,0,3,0,0,0 B: 0,0,0,0,3,3,2,1,5,6,7,0 Both class A and B strings exhibit variability. Many (e.g. ...
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0answers
24 views

Parameter selection in one class svm

Is there any accepted way to select the best parameters for one-class svm with a linear kernel in r? I have managed to run the svm function in e1071 but it is super sensitive to the model ...
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1answer
24 views

Comparing classification algorithms using cross validation and caret's train

I am having issues understanding some concepts of algorithm comparison/parameter optimization/cross-validation in R Let's say I want to compare two classification algorithms, such as Random Forests ...
2
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2answers
61 views

How is Naive Bayes a Linear Classifier?

I've seen the other thread here but I don't think the answer satisfied the actual question. What I have continually read is that Naive Bayes is a linear classifier (ex: here) (such that it draws a ...
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1answer
19 views

time series based classification

I want to classify some data. Basically the data is time series in nature. The target variable is categorical. I know there are so many algorithms for predicting the time series model. However, I have ...
2
votes
1answer
50 views

Correcting naïve Sensitivity and Specificity for classifier tested against imperfect gold standard

I am writing a supervised classifier for a particular condition. I have two sets of data for my "gold standard", against which I will test my classifier: a Positive set, in which all samples have ...
4
votes
2answers
29 views

How to include a pattern for'unknown' for an SVM classifier?

I am doing a classification of heart beat with SVM. There are five kinds of beats in my training data. I plan to add a new kind of data named 'unknown' beat. If there is no unknown beat, one ...
0
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0answers
19 views

Can a classification algorithm be used to measure the Clustering Quality (CQM)

Can a classifier be used to measure the Clustering Quality measure? I have come across this paper where the researcher uses a classifier like : five nearest neighbor classifier, a C4.5 decision ...
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1answer
17 views

Semistructured document classification

I am trying to cluster products based on the text descriptions of the products. I have millions of products. The nature of the products could be hierarchical. i.e; Clothing will have T-Shirts & ...
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0answers
30 views

The accuracy measure of a classification process

I have build the signal processing and feature extraction models, those features, inputed to Neural Network using matlab, which is give me the following performance measure,And I have several ...
0
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0answers
11 views

converting discrete values to buckets to perform predictions

I have a set of continuous discrete values, which I would like to convert to a classification task. Say, my scores in an exam are anything between 0-100. I want to convert my scores in the next exam ...
2
votes
1answer
158 views

A framework for multi-valued categorical attributes

In the scenario in which I'm working each entity could be represented in terms of N distinct properties that I will call p1, p2, ..., pn. For each of them, an entity, can have its specific range of ...
0
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1answer
29 views

prototypes and replacing missing variables in Random Forests algorithm

I'm new with Random forests Classification algorithm, and I have some questions about concepts confused me, What is the role of prototypes in the classification operation, Are they the core of the ...
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0answers
12 views

Recommendations for fitting a classifier on panel data to predict one step head forecasted class

I am predicting the binary class, i.e. if it's in top10 or not, of a security based upon it's performance using predictors from current time. So it's simply a cross sectional classifier. As of now ...
3
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0answers
49 views

Terrible logistic model gives perfect results? [duplicate]

So I'm playing around with logistic regression in R, using the mtcars dataset, and I decide to create a logistic regression model on the 'am' parameter (that is manual or automatic transmission for ...
0
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0answers
33 views

Lyrics classification with WEKA

I have a data set of roughly 62.000 lyrics, and I would like to label them as English or not English, because in the next step I would only need the English ones. I have understood that WEKA is a ...
0
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1answer
72 views

can you use Bayes' rule twice?

I'm trying to build a classifier, to model the likelihood of an event C, that depends on two other events, X and Y. I know that one can use Bayesian analysis if you have $P(X|C)$ and $P(Y|C)$ ...
1
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1answer
48 views

Boosted trees and Variable Interactions

How can one see in a Boosted trees classification model, which variables interact with each other and how much? I would like to make use o this in R gbm package if possible
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0answers
33 views

Machine learning algorithm/approach advice for a particular problem - multiclass classification?

First real meaningful dip into machine learning here for a project, and I'd like to optimize my time spent by getting the algorithms of choice nailed properly from the outset. Straight up however I'm ...
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0answers
21 views

Best approach to solve this time series classification problem

Hi I have a large data set that contains many time series signals which are labeled to classes, the time series signals look as in the following picture: The time series contain 2000 time samples, ...
3
votes
1answer
17 views

How to assess if more data would improve classification

I am trying to make an argument that if my field collected larger samples, we would be able to make better models with higher predictive accuracy. However, there's also the possibility that we are ...
0
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2answers
40 views

Datapoint Classification Accuracy

I am interested in finding ways to quantify the certainty of correct classificaions for single datapoints. This is interesting for me since for clinical studies where we for instance would classify ...
0
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0answers
15 views

Do i need equidistant time intervals for classification task?

I have a dataset of the following structure and want to use it for a classification task. The first column contains the timestamp when the values were measured. As you can see only data changes are ...
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0answers
12 views

For time series classification, how can k nearest neighbors outperform other models?

Suppose you have a collections of time series data $Y^1,\ldots,Y^N$, $Y^i = Y^i_1,\ldots,Y^i_T$. Your training data consists of labels for some of these $Y^i$, and you wish to infer labels for the ...