Support Vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems.
TF-IDF is often used in text mining and natural language processing tasks. SVM can be used in conjunction with TF-IDF for text categorization purposes.
The steps to use TF-IDF in a Support Vector Machine are:
Text Preprocessing: This involves cleaning the text data by removing noise and unnecessary words, normalizing the text by converting all words to lower case, and stemming/lemmatizing the words to reduce them to their root form.
Calculate TF-IDF Scores: Use the TF-IDF formula to calculate scores for each word in each document. The resulting matrix will have documents as rows and unique words as columns, with TF-IDF scores as values.
Feature Selection: Depending on the size of your dataset and computational resources, you may need to select a subset of features (words) that are most relevant for your task. This could be done using techniques like Chi-Square Test or Mutual Information.
Train SVM Classifier: Use the TF-IDF scores as input features to train an SVM classifier. The SVM algorithm tries to find a hyperplane in an N-dimensional space that distinctly classifies the data points.
TF-IDF and SVM can also be used for multi-classification problems where the objective is to categorize documents into more than two predefined classes. The basic process remains the same, but the SVM algorithm needs to be adapted for multi-class classification.
There are two main approaches to adapt SVM for multi-class problems:
One-vs-All (OvA): In this approach, one class is considered as positive and all other classes are considered as negative. For example, if there are four classes A, B, C, D; then four different SVMs would be trained:
One-vs-One (OvO): In this approach, an SVM is trained for every pair of classes. For example, if there are three classes A, B, C; then three different SVMs would be trained:
In both approaches, the class that gets voted most by the classifiers is chosen as the final class of the test point.
The choice between OvA and OvO depends on the specific problem and dataset. OvA is usually preferred when the number of classes is large since it requires fewer classifiers than OvO. However, OvO tends to perform better when there are imbalances in the number of instances for each class.