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
This function takes in a list of bags (data
), a list of labels for each bag (labels
), and a classifier object that has a predict
method. It then iterates through each bag and uses the classifier to predict the label for each instance in the bag. If more than half of the instances in the bag are positive, it classifies the bag as positive. Otherwise, it classifies the bag as negative. The function returns a list of predictions for each bag.
To use this function, you will need to provide a list of bags and a list of labels for each bag, as well as a classifier object that has a predict
method. You can then call the multi_instance_learning
function to classify the bags.