Questions tagged [classification]

An instance of supervised learning that identifies the category or categories which a new instance of dataset belongs.

Filter by
Sorted by
Tagged with
8
votes
1answer
2k views

Difference between tf-idf and tf with Random Forests

I am working on a text classification problem using Random Forest as classifiers, and a bag-of-words approach. I am using the basic implementation of Random Forests (the one present in scikit), that ...
3
votes
1answer
3k views

Combine multiple classifiers to build a multi-modal classifier

Suppose I am interested in classifying a set of instances composed by different content types, e.g.: a piece of text an image as relevant or ...
29
votes
4answers
12k views

Quick guide into training highly imbalanced data sets

I have a classification problem with approximately 1000 positive and 10000 negative samples in training set. So this data set is quite unbalanced. Plain random forest is just trying to mark all test ...
12
votes
3answers
6k views

Unstructured text classification

I'm going to classify unstructured text documents, namely web sites of unknown structure. The number of classes to which I am classifying is limited (at this point, I believe there is no more than ...
5
votes
1answer
5k views

Polynomial Kernel Parameters in SVMs

In SVMs the polynomial kernel is defined as: (scale * crossprod(x, y) + offset)^degree How do the scale and offset parameters affect the model and what range should they be in? (intuitively please) ...
10
votes
1answer
821 views

Libraries for (label propagation algorithms/frequent subgraph mining) for graphs in R

General description of the problem I have a graph where some vertices are labeled with a type with 3 or 4 possible values. For the other vertices, the type is unknown. My goal is to use the graph to ...
10
votes
4answers
258 views

Why might several types of models give almost identical results?

I've been analyzing a data set of ~400k records and 9 variables The dependent variable is binary. I've fitted a logistic regression, a regression tree, a random forest, and a gradient boosted tree. ...
19
votes
2answers
11k views

Text categorization: combining different kind of features

The problem I am tackling is categorizing short texts into multiple classes. My current approach is to use tf-idf weighted term frequencies and learn a simple linear classifier (logistic regression). ...
4
votes
4answers
7k views

Can I use unsupervised learning followed by supervised learning?

I have a question about classifying documents using supervised learning and unsupervised learning. For example: - I have a bunch of documents talking about football. As we know, football has a ...
12
votes
2answers
5k views

Sentiment data for Emoji

For experimenting we'd like to use the Emoji embedded in many Tweets as a ground truth/training data for simple quantitative senitment analysis. Tweets are usually too unstructured for NLP to work ...
20
votes
3answers
14k views

what is difference between text classification and topic models?

I know the difference between clustering and classification in machine learning, but I don't understand the difference between text classification and topic modeling for documents. Can I use topic ...
5
votes
1answer
1k views

Classifying Java exceptions

We have a classification algorithm to categorize Java exceptions in Production. This algorithm is based on hierarchical human defined rules so when a bunch of text forming an exception comes up, it ...
3
votes
1answer
58 views

Can I classify set of documents using classifying method using limited number of concepts ?

I have set of documents and I want classify them to true and false My question is I have to take the whole words in the documents then I classify them depend on the similarity words in these ...
7
votes
6answers
1k views

Which cross-validation type best suits to binary classification problem

Data set looks like: 25000 observations up to 15 predictors of different types: numeric, multi-class categorical, binary target variable is binary Which cross validation method is typical for this ...
16
votes
2answers
9k views

How to increase accuracy of classifiers?

I am using OpenCV letter_recog.cpp example to experiment on random trees and other classifiers. This example has implementations of six classifiers - random trees, boosting, MLP, kNN, naive Bayes and ...
41
votes
6answers
24k views

Cosine similarity versus dot product as distance metrics

It looks like the cosine similarity of two features is just their dot product scaled by the product of their magnitudes. When does cosine similarity make a better distance metric than the dot product? ...
8
votes
4answers
3k views

Skewed multi-class data

I have a dataset which contains ~100,000 samples of 50 classes. I have been using SVM with an RBF kernel to train and predict new data. The problem though is the dataset is skewed towards different ...
6
votes
1answer
93 views

How to define a custom resampling methodology

I'm using an experimental design to test the robustness of different classification methods, and now I'm searching for the correct definition of such design. I'm creating different subsets of the ...
11
votes
3answers
2k views

Build a binary classifier with only positive and unlabeled data

I have 2 datasets, one with positive instances of what I would like to detect, and one with unlabeled instances. What methods can I use ? As an example, suppose we want to understand detect spam ...
28
votes
4answers
25k 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: ...
4
votes
1answer
599 views

How are selected the features for a decision tree in CART?

Suppose I want to use CART as classification tree (I want a categorical response). I have the training set, and I split it using observation labels. Now, to build the decision tree (classification ...
4
votes
3answers
270 views

Which non-training classification methods are available?

I am trying to find which classification methods, that do not use a training phase, are available. The scenario is gene expression based classification, in which you have a matrix of gene expression ...
4
votes
1answer
140 views

Large Scale Personalization - Per User vs Global Models

I'm currently working on a project that would benefit from personalized predictions. Given an input document, a set of output documents, and a history of user behavior, I'd like to predict which of ...
12
votes
3answers
272 views

Measuring performance of different classifiers with different sample sizes

I'm currently using several different classifiers on various entities extracted from text, and using precision/recall as a summary of how well each separate classifier performs across a given dataset. ...
5
votes
4answers
507 views

How does Google categorize results from its image search?

While doing a Google image search, the page displays some figured out categories for the images of the topic being searched for. I'm interested in learning how this works, and how it chooses and ...
14
votes
2answers
6k views

Binary classification model for unbalanced data

I have a dataset with the following specifications: Training dataset with 193,176 samples with 2,821 positives Test Dataset with 82,887 samples with 673 positives There are 10 features. I want to ...
7
votes
1answer
190 views

Dealing with diverse text data

I'm currently working with a dataset with a wide range of document lengths -- anywhere from a single word to a full page of text. In addition, the grammatical structure and use of punctuation varies ...
9
votes
4answers
3k views

Suggest text classifier training datasets

Which freely available datasets can I use to train a text classifier? We are trying to enhance our users engagement by recommending the most related content for him, so we thought If we classified ...
12
votes
1answer
416 views

What is the difference between global and universal compression methods?

I understand that compression methods may be split into two main sets: global local The first set works regardless of the data being processed, i.e., they do not rely on any characteristic of the ...
9
votes
1answer
296 views

Relational Data Mining without ILP

I have a huge dataset from a relational database which I need to create a classification model for. Normally for this situation I would use Inductive Logic Programming (ILP), but due to special ...
12
votes
3answers
7k views

Best way to classify datasets with mixed types of attributes

I would like to know what is the best way to classify a data set composed of mixed types of attributes, for example, textual and numerical. I know I can convert textual to boolean, but the vocabulary ...
15
votes
5answers
24k views

Choose binary classification algorithm

I have a binary classification problem: Approximately 1000 samples in training set 10 attributes, including binary, numeric and categorical Which algorithm is the best choice for this type of ...
16
votes
3answers
2k views

One-Class discriminatory classification with imbalanced, heterogenous Negative background?

I'm working on improving an existing supervised classifier, for classifying {protein} sequences as belonging to a specific class (Neuropeptide hormone precursors), or not. There are about 1,150 known ...
8
votes
2answers
3k views

How to get an aggregate confusion matrix from n different classifications

I want to test the accuracy of a methodology. I ran it ~400 times, and I got a different classification for each run. I also have the ground truth, i.e., the real classification to test against. For ...
11
votes
4answers
6k views

Algorithm for generating classification rules

So we have potential for a machine learning application that fits fairly neatly into the traditional problem domain solved by classifiers, i.e., we have a set of attributes describing an item and a "...
8
votes
1answer
290 views

Using SVM as a binary classifier, is the label for a data point chosen by consensus?

I'm learning Support Vector Machines, and I'm unable to understand how a class label is chosen for a data point in a binary classifier. Is it chosen by consensus with respect to the classification in ...