Simple Multi Instance Learning Program Using Keras

Multi-instance learning is a type of machine learning where the input data is organized into sets or bags, and each bag contains multiple instances or examples. The goal of multi-instance learning is to classify the bag as a whole based on the instances it contains, rather than classifying each instance individually.

Here is an example of a simple multi-instance learning algorithm in Python:

def multi_instance_learning(data, labels, classifier):
  # Initialize the prediction list
  predictions = []
  
  # Iterate through each bag in the data
  for bag, label in zip(data, labels):
    # Initialize the bag prediction to 0
    bag_prediction = 0
    
    # Iterate through each instance in the bag
    for instance in bag:
      # Use the classifier to predict the label for the instance
      instance_prediction = classifier.predict(instance)
      
      # If the instance is positive (1), increment the bag prediction
      if instance_prediction == 1:
        bag_prediction += 1
    
    # If the number of positive instances in the bag is greater than half, classify the bag as positive (1)
    if bag_prediction > len(bag) / 2:
      predictions.append(1)
    else:
      predictions.append(0)
      
  return predictions