1
vote
1answer
45 views

For a model like this what performance measures can I calculate and how?

Methods: From the machine learning literature, I understand different parameters can show performance of model in machine learning. I would briefly expand my understanding with confusion matrix: ...
1
vote
2answers
112 views

High precision with low recall SVM

I'm classifying a data set using SVM and those are the precision and recall values for two classes. ...
1
vote
1answer
17 views

SVM Classification with Duplicate Training Instances

I'm using SVMs with linear kernel for sentence classification (binary). My dataset contains many duplicate instances i.e. many sentences in the training set have identical feature vectors. In the ...
1
vote
1answer
49 views

Simple SVM Question

For a linear SVM, the documentation tells me the formula is: $$ \frac{1}{2}w^Tw+C\sum\limits_{i=1}^l\xi_i$$ Please explain to me in layman's terms what w (and ξ) represent. Is w the distance to the ...
0
votes
0answers
16 views

Softmax regression or $K$ binary logistic regression

For a multi-class classification problem, we can use $K$ binary logistic classifiers, or one softmax regression classifier, so how to make the choice between the two? IMHO, the $K$ binary logistic ...
-2
votes
0answers
9 views

Which classifier is the best for short text classification, Naive Bays or RBFN? [closed]

I am doing my M.E. project on short text classification for Online Social Networks.I am using Weka for classification.Please tell me between Naive Bays and RBFN which classifier is the good one for ...
-1
votes
0answers
11 views

Naive Bays classifier showing results for precision, recall and F value is always 1

I am implementing Naive Bays text classifier using Weka. I have trained it with very few words (about 20). I am getting the result that precision, recall and f value all as 1. Is this possible? ...
3
votes
3answers
51 views

Recognition of simple patterns and prediction

I have been doing supervised learning and classification with multilayer perceptron for some time. But now I need to use unsupervised learning to infer the presence of a pattern and I would need some ...
2
votes
1answer
76 views

What is the difference between a multi-label and a multi-class classification?

What is the difference between multi-label classification and multiclass classfication. Speficially, what is the difference between a label and a class? Please provide a clear example. "Multiclass ...
1
vote
0answers
21 views

Binary classification of dated text documents with seasonality

I have a collection of training documents with publication dates, where each document is labeled as belonging (or not) to some topic T. I want to train a model that will predict for a new document ...
0
votes
0answers
28 views

What is the simplest way to classify airplan manuvers?

Suppose we have declared four motion types for air-plane. If we represent each maneuver with a trajectory line, what is the best classification method to retrieve the trajectory pattern with a similar ...
4
votes
1answer
95 views

Is KNN a discriminative learning algorithm?

It seems that KNN is a discriminative learning algorithm but I can't seem to find any online sources confirming this. Is KNN a discriminative learning algorithm?
0
votes
1answer
30 views

SVM cost parameter

In a SVM with linear kernel, could you explain to me what exactly the C parameter is/represents? An example why it's important to select a good value for C would also be appreciated. Thank you.
1
vote
1answer
58 views

Maximum Entropy Model for classification, what to use as context & feature?

I'm building a Maximum Entropy Model to classify some text, based on paper "A Maximum Entropy Approach to Natural Language Processing" by Berger et.al. It's similar to POS tagging. Below is some ...
0
votes
0answers
32 views

Training and testing on Unbalanced Data Set

I used SMOTE algorithm in R for class balancing. My data size has 13000 rows, I had 7% minority class in my sample now I used SMOTE( Synthetic Minority Oversampling Technique) for class balancing such ...
0
votes
1answer
22 views

how to handle (many) false positives in training dataset for logistic regression classifier

I want to train a logistic regression dataset. I have a quite big training data set ( >100 000) and have around 10 features I can train on. Half of my training data is negative training data and I ...
3
votes
1answer
39 views

What is the difference between a “learner” and “classifier” in supervised learning?

This question stems from Pedro Domingos' excellent paper "A Few Useful Things to Know About Machine Learning." The paper is extremely clear and well-written, but I still have a clarification question. ...
0
votes
1answer
36 views

Way to train Hidden Markov Model in R with multiple sequences

i have multiple sequences for each of two states. I'd like to train a HMM with these to predict the state for unkown sequences. Here is an example for this problem: ...
0
votes
0answers
42 views

What algorithms should I use to perform job classification based on resume data?

Note that I am doing everything in R. The problem goes as follow: Basically, I have a list of resumes (CVs). Some candidates will have work experience before and some don't. The goal here is to: ...
-2
votes
0answers
22 views

analyzing neturalnet function from R [duplicate]

'neuralnet' package in R allows us to use neural network algorithm with back propagation. I want to use the function for prediction. I saw a tutorial on neuralnet in which iris data was predicted. I ...
0
votes
1answer
44 views

How do I detect state change in multivariate time series?

I have a multivariate time series . For each row in the data we have the values of inputs and a label for stability (0 or 1 ) . What are the algorithms that can detect the stability for an unlabelled ...
0
votes
2answers
44 views

Best feature selection method for naive Bayes classification

i want to make classification with naive Bayes. I have got about 100 Features. Numerical ones as well as categorical ones. Since i want only the most relevant ones to be included for the ...
0
votes
0answers
13 views

Correlation between sensors

Background: A home wired with multiple sensors, measuring attributes like temperature, light, motion etc. In addition, a multitude of actuators that can carry out an action like opening a door, ...
0
votes
0answers
8 views

Regularization in multinomial manifold (k-simplex) space

I have a multi-class classification problem. In order to reduce the feature space, I have mapped my weights ($w$) to a multinomial manifold (or k-simplex) and compute the new weights ($v$) as follows: ...
2
votes
3answers
137 views

Naive Bayes: Imbalanced Dataset in Real-time Scenario

I am using scikit-learn Multinomial Naive Bayes classifier for binary text classification (classifier tells me whether the document belongs to the category X or not). I use a balanced dataset to train ...
1
vote
0answers
22 views

Open, multi-class , active learning classfiers

I am trying to classify text documents using a huge corpora. Thats a huge tagset (more than 1000 tags). Corpus will have 1000 samples for each tag. But the tagset is not closed. New tags can be added ...
0
votes
0answers
18 views

Classification More Robust Than Regression

Are neural networks using classification more robust / reliable than using regression to produce a single value? The only reason I would think so is that it would be easier for the network to adjust ...
0
votes
1answer
13 views

Solving feature bias issues in Learning to Rank with implicit feedback

I have a learning to rank system where implicit feedback (from user clicks) is used to determine +ve and -ve examples for the training. The problem is that (obviously) the learner sees only the top ...
1
vote
1answer
48 views

Reverse AUC interpretation

Given a classifier (SVM) classifying in 2 'classes' (+1 or -1) for prediction purposes. It has an AUC score of 0.28, meaning its success rate is lower than just random predictions. If I just do the ...
1
vote
0answers
57 views

What should I use - Multi label classification or Multi class classification?

In my dataset, I have 2 labels, positive and negative. Most samples belong to only one class, either positive or negative. A small fraction of samples take both labels i.e. both positive and negative. ...
0
votes
0answers
22 views

BOW prediction of cluster for training data

I am creating a bag of visual words for classification of videos. I am not using SURF descriptors and that is why I couldn't use OpenCV's BOWImgDescriptorExtractor ...
2
votes
2answers
54 views

Cluster Data based on Distribution

I have a list of diseases for my research. For each disease, I have a list of ages for the diseases. "Breast Carcinoma" may be a list of [1,2,2,3,4,5,5,5,5,5] while "Adrenal Cortex Neoplasms" maybe be ...
1
vote
0answers
17 views

How to combine n confusion matrices

Suppose you have $n$ confusion matrices, each from a different independent classification on a dataset. Suppose each classification is ran on a proper subset of the dataset, and usually these subsets ...
0
votes
0answers
24 views

Supervised classification on different time series

I have 300 files, each file has a time series data with a class label(0 or1) for each data point.I want to build a classifier, which can predict the class of a new time series data. How should I ...
2
votes
0answers
46 views

How to calculate accuracy of each feature

I read some paper (* about predicting user retention in StumbledUpon) and saw the authors provide a list of features with accuracy of each feature with the following explanation: As decision ...
0
votes
1answer
33 views

How is the training set constructed for multi-class SVMs?

Support vector machines do binary classification. If there is more than two classes, it is possible to train several classifiers instead of one. Two common approaches are training one vs. one (each ...
0
votes
0answers
20 views

What's the safest way to resample to ensure equal class frequencies in training data?

I am working on a number of EEG data sets for binary classification. A good example of one is publicly available here. If you look at what Matthias Kaper did to classify that set, one thing that ...
0
votes
0answers
44 views

R choosing the right classification approach for $ transaction volume categories

Our customers are Merchants and use our online payment service. Before they started using our service, they indicated how much $ transaction volume they will make per year. However this turned out to ...
1
vote
1answer
52 views

Is f-measure synonymous with accuracy?

I understand that f-measure (based on precision and recall) is an estimate of how accurate a classifier is. Also, f-measure is favored over accuracy when we have an unbalanced dataset. I have a simple ...
2
votes
1answer
59 views

Why we need single class classification?

I have started learning classification in machine learning. I face two terminologies, one is "single class classification" and a the other is "binary class classification". I am confused about when ...
3
votes
1answer
69 views

Benefits of CART over ID3 algorithm

When building decision trees over a dataset that generates nodes with bad purity, is there any benefit of using the CART algorithm over the iterative dichotomizer 3 (ID3) algorithm?
0
votes
0answers
36 views

Should I use multi-label classification?

I have a classification problem with 2 classes (positive and negative). Usually, in such classification problems, all the samples will be labelled either 'positive' or 'negative'. In my dataset, some ...
3
votes
3answers
82 views

Alternatives to bag-of-words based classifiers for text classification?

Most of the text classifiers are based on the bag-of-words approach where you loose the context that a particular word appears. As a solution (or simple solution?) we can use n-grams as features. But ...
1
vote
0answers
33 views

Hierarchiqual prediction using R

I'm pretty new in R, and I couldn't find any information about a package who can do the following: supposing that I have a set of data (for instance, different text documents), which can have several ...
2
votes
2answers
75 views

Suggestions for cost-sensitive learning in a highly imbalanced setting

I have a dataset with a few million rows and ~100 columns. I would like to detect about 1% of the examples in the dataset, which belong to a common class. I have a minimum precision constraint, but ...
0
votes
2answers
84 views

Handling unbalanced data using SMOTE - NO BIG DIFFERENCE?

I have a classification problem with 2 classes. I have nearly 5000 samples, each of which is represented as vector with 570 features. The positive class samples are nearly 600. Meaning, I have a 1:8 ...
0
votes
0answers
56 views

Set up a classification experiment via libsvm (MATLAB)

I would like to set up an experiment in MATLAB, to predict the class of a set of text instances in a two-class problem (e.g., the text talks/does not talk about ...
0
votes
0answers
29 views

Time series image data classification / video image classification

I am working on classifying video frames into two classes, positive and negative. e.g. if a particular pattern appears in a frame that frame will be classified into positive, otherwise negative. But ...
3
votes
1answer
91 views

Machine learning classifiers big-O or complexity

To evaluate the performance a new classifier algorithm, I'm trying to compare the accuracy and the complexity (big-O in training and classifying). From Machine Learning: a review I get a complete ...
0
votes
0answers
27 views

Friedman's test to identify best of multiple classifiers on multiple domains

I have several classifiers f_i (i=1..N) and calculated performance measures on multiple domains (D) for each. Thus, there are NxD values. I want to find out (increasing complexity): Is a particular ...