Notes - MCS
Machine Learning Applied to Security
Notes - MCS
Machine Learning Applied to Security
  • Machine Learning Applied to Security
  • Machine Learning
    • AI and ML
    • Taxonomy
    • Limitations
    • Terminology
  • SPAM
    • SPAM
    • SPAM Detection
    • Classification Model
    • Naive Bayes (Discrete)
    • SPAM or HAM
    • Blind Optimization
    • Gradient descent
    • Linear Regression
    • Logistic Regression
    • Binary Classification
  • Anomaly Detection
    • Context
    • Anomaly Detection
      • Examples
      • Detection
      • Techniques
    • Detecting anomalies just by seeing
    • Unsupervised Learning
    • Autoencoders
    • Isolation Forest
    • Local Outlier Factor
    • One-Class SVM
    • Tips
  • Malware Detection
    • Context
    • Creeper virus
    • ILOVEYOU worm
    • CryptoLocker ransomware
    • Mirai botnet
    • Clop ransomware
    • How To Recognize Malware
    • Malware Detection
    • Machine Learning Approaches
    • Requirements
    • Multi-Class Classification
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  • One-Vs-Rest (One-Vs-All)
  • One-Vs-One
  1. Malware Detection

Multi-Class Classification

Last updated 1 year ago

In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification).

One-Vs-Rest (One-Vs-All)

It involves splitting the multi-class dataset into multiple binary classification problems. A binary classifier is then trained on each binary classification problem and predictions are made using the model that is the most confident.

  • Binary Classification Problem 1: red vs [blue, green]

  • Binary Classification Problem 2: blue vs [red, green]

  • Binary Classification Problem 3: green vs [red, blue]

One-Vs-One

The formula for calculating the number of binary datasets, and in turn, models, is as follows: (NumClasses∗(NumClasses−1))/2(NumClasses * (NumClasses-1)) / 2(NumClasses∗(NumClasses−1))/2 Each binary classification model may predict one class label, and the model with the most predictions or votes is predicted by the one-vs-one strategy.

  • Binary Classification Problem 1: red vs. blue

  • Binary Classification Problem 2: red vs. green

  • Binary Classification Problem 3: red vs. yellow

  • Binary Classification Problem 4: blue vs. green

  • Binary Classification Problem 5: blue vs. yellow

  • Binary Classification Problem 6: green vs. yellow