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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1answer
8 views

Classification on variable-length time series

I have a series of transactions like the following: [0, 2, 2, 3, 1, 0, 0, 0, 1] [1, 0, 0] [3, 3, 1, 1] I would like to classify each transaction as being part of one of two categories: class A or ...
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1answer
15 views

Valid result when adding two kernels with negative coefficient?

If $k_1$ and $k_2$ be a kernel in $ \mathbb{R}^n \times \mathbb{R}^n $. we know $k(x,z)=ak_1(x,z) + bk_2(x,z)$ (kernel addition) is still a valid kernel if $\: a,b \geq 0\,$ ($a,b$ is real numbers, ...
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2answers
97 views

Applying PCA to test data for classification purposes

I've recently learned about the wonderful PCA and I've done the example outlined in scikit-learn documentation. I am interested to know how I can apply PCA to new data points for classification ...
2
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1answer
23 views

Statistical Classification Method to Compare PURELY Categorical Data?

I have about a half-dozen variables, each of which can have anywhere from three to ten outcomes. I have to measure the degree of separation/similarity between rows. Either we can do some sort of ...
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0answers
13 views

Sampling from Study Population for Labeling

I have the opportunity to get labels for a portion of my study population, and I can determine the subjects that will receive a label. Every subject in the population can be targeted. The labels are ...
4
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0answers
31 views

two margin comparison and one conclusion?

I read following notes, and couldn't get it. any idea or hint would highly appreciated. a SVM classifier using a second order polynomial kernel. The first polynomial kernel maps each input data x to ...
2
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1answer
21 views

variance in test accuracy will increase as we increase the number of test examples؟

I see this statement on 1 that say a True statement on Machine Learning Context. The variance in test accuracy will increase as we increase the number of test examples. my challenge is why ...
3
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1answer
48 views

Simple Question about ROC Curve

Motivated by this reference, it states under ROC Space When evaluating a binary classifier, we often use a Confusion Matrix...however here we need only TPR and FPR I'd feel more comfortable if ...
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1answer
115 views
+100

calculation threshold for minimum risk classifier?

Suppose Two Class $C_1$ and $C_2$ has an attribute $x$ and has distribution $ \cal{N} (0, 0.5)$ and $ \cal{N} (1, 0.5)$. if we have equal prior $P(C_1)=P(C_2)=0.5$ for following cost matrix: $L= ...
3
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1answer
75 views

some inference about k-NN algorithms for better understanding? [on hold]

I ran into some facts make me confusing. for k-NN classifier: I) why classification accuracy is not better with large values of k. II) the decision boundary is not smoother with smaller ...
2
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0answers
27 views

Sample lower bound for binary classification in Linear Discriminant Analysis?

Below is a description of this problem: Suppose the label $Y\in\{1,0\}$ in binary classification satisfies $\Pr[Y=1]=\Pr[Y=0]=\frac{1}{2}$, and $p(X|Y=1)=\mathcal{N}(\mu_1,\Sigma)$, ...
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92 views
+50

How we can calculate Fisher criterion weights?

I studying for Pattern Recognition and ML. I ran to one type of question: We define equal prior probability as: $P(D_1)=P(D_2)= \frac{1}{2}$ in two-class classification problem, if the ...
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0answers
82 views

How many features will be selected by mutual information and wrapper in information filtering?

I see one example in old-mid exam from well-known person Tom Mitchell, as follows: Consider learning a classifier in a situation with 1000 features total. 50 ...
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21 views

What is the commonly used mehtod for measuring variance of accuracy mean using k-fold cross validation?

I know there are planty of questions about standard deviation, though I didn't find an answer tuned to my particular need and also I could really use your help! I'm performing 18-Fold Cross ...
5
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1answer
37 views

a challenge with linear classification and distance to origin? [on hold]

I ran into a problem, when studying on linear classification. my prof. says: in a linear classification $y=w_0+w_1x_1+w_2x_2$ that depicted on following figure, distance of origin to decision ...
0
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0answers
12 views

graph classification task - multi label?

I have a data set in graph format representing semantic connection between terms. The data set is divided into clusters, each with several labels (not unique, or mutually exclusive, no set number of ...
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0answers
17 views

Sample size needed to validate classification/prediction model

Dose any rule of thumb exist (or possible calculation) regarding sample size needed to validate an binary classification model. We have developed this prediction model for a medical condition and ...
1
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1answer
15 views

Skewed Classification Problem

So I've read around and seen this is a problem. I have a classification problem and 12 variables ... I'm working on getting more, but even if l get the number to 20-30 I feel like the problem will ...
1
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1answer
174 views
+50

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 ...
0
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0answers
14 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
21 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
43 views
+50

How to use information about likelihood of classes in a classifier?

General question: How can information about the likelihood of classes be used to improve a classifier? Suppose that the probability of each class is known quite precisely (from a very large sample), ...
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0answers
29 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 ...
0
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1answer
40 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 ...
3
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2answers
57 views

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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1answer
52 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 ...
1
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0answers
38 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
81 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 ...
0
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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 ...
0
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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 ...
1
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0answers
18 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 ...
0
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1answer
15 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 ...
0
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1answer
26 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. ...
1
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2answers
48 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 ...
0
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1answer
27 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 ...
0
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3answers
35 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 ...
0
votes
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
votes
1answer
28 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 ...
1
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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. ...
1
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0answers
26 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 ...
0
votes
1answer
27 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
votes
2answers
62 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 ...
0
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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
31 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
21 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 ...
0
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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 & ...
0
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0answers
33 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 ...