TRAINING A HIDDEN MARKOV MODEL

• US 20180260735A1
• Filed: 03/08/2017
• Published: 09/13/2018
• Est. Priority Date: 03/08/2017
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Arad, Omer, Mashkif, Nir, Masin, Michael, Zadorojniy, Alexander, Zeltyn, Sergey
• Application Number:
  US15/452,749
• Publication Number:
  US 20180260735A1
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A computer program product, an apparatus and a method for training of an HMM. The method comprises applying a classifier that uses an HMM which was trained based on a training set, on a set of samples to provide an initial prediction; computing a first F1-score of the initial prediction measuring an accuracy of the initial prediction; selecting a misclassified sample by the classifier in the initial prediction; adding the misclassified sample to the training set; training the HMM using the misclassified sample to provide a modified HMM; applying the classifier using the modified HMM on the set of samples to provide a second prediction; computing a second F1-score of the second prediction; and comparing the first F1-score and the second F1-score; in response to a determination that the first F1-score is greater than the second F₁-score, removing the misclassified sample from the training set.

Claims

1. A computer program product comprising a non-transitory computer readable storage medium retaining program instructions, which program instructions when read by a processor, cause the processor to perform the steps of:

obtaining a set of samples and labels thereof;

applying a Hidden Markov Model (HMM)-based classifier on the set of samples to obtain a set of predicted labels, whereby providing an initial prediction, wherein the HMM-based classifier is configured to utilize an HMM to predict a label for a sample, wherein the HMM is trained based on a training set;

computing a first F₁-score of the initial prediction, wherein the first F₁-score measures an accuracy of the initial prediction by comparing the predicted labels and the labels of the set of samples;

selecting a misclassified sample from the set of samples, wherein the misclassified sample is a sample that is misclassified by the HMM-based classifier in the initial prediction;

adding the misclassified sample to the training set;

in response to said adding, training the HMM based on the training set, whereby providing a modified HMM;

applying the HMM-based classifier using the modified HMM on the set of samples to obtain a second set of predicted labels, whereby providing a second prediction;

computing a second F₁-score of the second prediction; and

comparing the first F₁-score and the second F₁-score, wherein in response to a determination that the first F₁-score is greater than the second F₁-score, removing the misclassified sample from the training set.

2. The computer program product of claim 1, wherein the program instructions are further adapted to cause the processor to:

iteratively perform said computing the first f₁-score, said selecting, said adding, said training, said applying, said computing the second f₁-score, and said comparing.

3. The computer program product of claim 2, wherein in response to a determination, in a first iteration, that the second F₁-score is greater than the first F₁-score, utilizing the modified HMM in a second iteration, wherein the second iteration follows the first iteration.

4. The computer program product of claim 2, wherein in response to a determination, in a first iteration, that the second F₁-score is greater than the first F₁-score, removing the misclassified sample from the set of samples, whereby said selecting in a second iteration is performed from a reduced set of samples, wherein the second iteration follows the first iteration.

5. The computer program product of claim 1, wherein the set of samples is obtained from a first source, wherein the training set is obtained from a second source, wherein the first source is different than the second source.

6. The computer program product of claim 5, wherein the set of samples comprises private data samples, wherein the private data samples are non-disclosable to the second source, whereby enhancing prediction accuracy of the HMM-based classifier based on the private data samples.

7. The computer program product of claim 5, wherein the second source is a distributer of the HMM, wherein the first source is an entity utilizing the HMM obtained from the distributer, whereby the entity personalizes the HMM based on the set of samples of the entity.

8. A computer implemented method comprising:

obtaining a set of samples and labels thereof;

applying a Hidden Markov Model (HMM)-based classifier on the set of samples to obtain a set of predicted labels, whereby providing an initial prediction, wherein the HMM-based classifier is configured to utilize an HMM to predict a label for a sample, wherein the HMM is trained based on a training set;

computing a first F₁-score of the initial prediction, wherein the first F₁-score measures an accuracy of the initial prediction by comparing the predicted labels and the labels of the set of samples;

selecting a misclassified sample from the set of samples, wherein the misclassified sample is a sample that is misclassified by the HMM-based classifier in the initial prediction;

adding the misclassified sample to the training set;

in response to said adding, training the HMM based on the training set, whereby providing a modified HMM;

applying the HMM-based classifier using the modified HMM on the set of samples to obtain a second set of predicted labels, whereby providing a second prediction;

computing a second F₁-score of the second prediction; and

comparing the first F₁-score and the second F₁-score, wherein in response to a determination that the first F₁-score is greater than the second F₁-score, removing the misclassified sample from the training set.

9. The computer implemented method of claim 8 further comprising:

iteratively performing said computing the first f₁-score, said selecting, said adding, said training, said applying, said computing the second f₁-score, and said comparing.

10. The computer implemented method of claim 9 wherein in response to a determination, in a first iteration, that the second F₁-score is greater than the first F₁-score, utilizing the modified HMM in a second iteration, wherein the second iteration follows the first iteration.

11. The computer implemented method of claim 9, wherein in response to a determination, in a first iteration, that the second F₁-score is greater than the first F₁-score, removing the misclassified sample from the set of samples, whereby said selecting in a second iteration is performed from a reduced set of samples, wherein the second iteration follows the first iteration.

12. The computer implemented method of claim 8, wherein the set of samples is obtained from a first source, wherein the training set is obtained from a second source, wherein the first source is different than the second source.

13. The implemented method of claim 12, wherein the set of samples comprises private data samples, wherein the private data samples are non-disclosable to the second source, whereby enhancing prediction accuracy of the HMM-based classifier based on the private data samples.

14. The implemented method of claim 12, wherein the second source is a distributer of the HMM, wherein the first source is an entity utilizing the HMM obtained from the distributer, whereby the entity personalizes the HMM based on the set of samples of the entity.

15. A computerized apparatus having a processor, the processor being adapted to perform the steps of:

obtaining a set of samples and labels thereof;

applying a Hidden Markov Model (HMM)-based classifier on the set of samples to obtain a set of predicted labels, whereby providing an initial prediction, wherein the HMM-based classifier is configured to utilize an HMM to predict a label for a sample, wherein the HMM is trained based on a training set;

computing a first F₁-score of the initial prediction, wherein the first F₁-score measures an accuracy of the initial prediction by comparing the predicted labels and the labels of the set of samples;

selecting a misclassified sample from the set of samples, wherein the misclassified sample is a sample that is misclassified by the HMM-based classifier in the initial prediction;

adding the misclassified sample to the training set;

in response to said adding, training the HMM based on the training set, whereby providing a modified HMM;

applying the HMM-based classifier using the modified HMM on the set of samples to obtain a second set of predicted labels, whereby providing a second prediction;

computing a second F₁-score of the second prediction; and

comparing the first F₁-score and the second F₁-score, wherein in response to a determination that the first F₁-score is greater than the second F₁-score, removing the misclassified sample from the training set.

16. The computerized apparatus of claim 15, wherein the processor is further adapted to:

iteratively perform said computing the first f₁-score, said selecting, said adding, said training, said applying, said computing the second f₁-score, and said comparing.

17. The computerized apparatus of claim 16, wherein in response to a determination, in a first iteration, that the second F₁-score is greater than the first F₁-score, removing the misclassified sample from the set of samples, whereby said selecting in a second iteration is performed from a reduced set of samples, wherein the second iteration follows the first iteration.

18. The computerized apparatus of claim 15, wherein the set of samples is obtained from a first source, wherein the training set is obtained from a second source, wherein the first source is different than the second source.

19. The computerized apparatus of claim 18, wherein the set of samples comprises private data samples, wherein the private data samples are non-disclosable to the second source, whereby enhancing prediction accuracy of the HMM-based classifier based on the private data samples.

20. The computerized apparatus of claim 18, wherein the second source is a distributer of the HMM, wherein the first source is an entity utilizing the HMM obtained from the distributer, whereby the entity personalizes the HMM based on the set of samples of the entity.