PU Classification Device, PU Classification Method, and Recording Medium

A PU classification method comprising:

• learning a classifier that performs maximum likelihood classification of an instance to be classified as a positive instance or a negative instance based on a magnitude relationship between a first probability that the instance is sampled from a population distribution for learning as the positive instance and a second probability that the instance is sampled from the population distribution for learning, when the instance to be classified is given, by estimating a distribution function of the first probability from a set of positive instances sampled from the population distribution for learning and by estimating a distribution function of the second probability from a set of instances that are sampled from the population distribution for learning and are unknown whether they are positive or negative;
• and
• classifying an instance to be classified as the positive instance or the negative instance by using the learned classifier.

A non-transitory computer readable recording medium storing a PU classification program, the PU classification program comprising:

• causing a computer to learn a classifier that performs maximum likelihood classification of an instance to be classified as a positive instance or a negative instance based on a magnitude relationship between a first probability that the instance is sampled from a population distribution for learning as the positive instance and a second probability that the instance is sampled from the population distribution for learning, when the instance to be classified is given, by estimating a distribution function of the first probability from a set of positive instances sampled from the population distribution for learning and by estimating a distribution function of the second probability from a set of instances that are sampled from the population distribution for learning and are unknown whether they are positive or negative;
• and
• causing the computer to classify an instance to be classified as the positive instance or the negative instance by using the learned classifier.