Вісники НТУ "ХПІ"

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Adaptation of LambdaMART model to semi-supervised learning
(Національний технічний університет "Харківський політехнічний інститут", 2023) Yamkovyi, Klym Serhiyovych
The problem of information searching is very common in the age of the internet and Big Data. Usually, there are huge collections of documents and only multiple percent of them are relevant. In this setup brute-force methods are useless. Search engines help to solve this problem optimally. Most engines are based on learning to rank methods, i.e. first of all algorithm produce scores for documents based on they feature and after that sorts them according to the score in an appropriate order. There are a lot of algorithms in this area, but one of the most fastest and a robust algorithm for ranking is LambdaMART. This algorithm is based on boosting and developed only for supervised learning, where each document in the collection has a rank estimated by an expert. But usually, in this area, collections contain tons of documents and their annotation requires a lot of resources like time, money, experts, etc. In this case, semi-supervised learning is a powerful approach. Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. Unlabeled data, when used in combination with a small quantity of labeled data, can produce significant improvement in learning accuracy. This paper is dedicated to the adaptation of LambdaMART to semi-supervised learning. The author proposes to add different weights for labeled and unlabeled data during the training procedure to achieve higher robustness and accuracy. The proposed algorithm was implemented using Python programming language and LightGBM framework that already has supervised the implementation of LambdaMART. For testing purposes, multiple datasets were used. One synthetic 2D dataset for a visual explanation of results and two real-world datasets MSLR-WEB10K by Microsoft and Yahoo LTRC.
Failure rate regression model building from aggregated data using kernel-based machine learning
(Національний технічний університет "Харківський політехнічний інститут", 2022) Akhiiezer, Olena Borisivna; Grinberg, Galyna Leonidivna; Lyubchyk, Leonid Mykhailovych; Yamkovyi, Klym Serhiyovych
The problem of regression model building of equipment failure rate using datasets containing information on number of failures of recoverable systems and measurements of technological and operational factors affecting the reliability of production system is considered. This problem is important for choosing optimal strategy for preventive maintenance and restoration of elements of process equipment, which, in turn, significantly affects the efficiency of production management system. From a practical point of view, of greatest interest is the development of methods for regression models building to assess the impact of various technological and operational factors controlled during system operation on failure rate. The usual approach to regression models construction involves preselecting the model structure in the form of a parameterized functional relationship between failure rate and affecting technological variables followed by statistical estimation of unknown model parameters or training the model on datasets of measured covariates and failures.The main problem lies precisely in the choice of model structure, the complexity of which should correspond to amount of data available for training model, which in the problem of failure rate modeling is greatly complicated by lack of a priori information about its dependence on affecting variables. In this work, such a problem is solved using machine learning methods, namely, kernel ridge regression, which makes it possible to effectively approximate complex nonlinear dependences of equipment failure rate on technological factors, while there is no need to pre-select the model structure. Preliminary aggregation of data by combination of factor and cluster analysis can significantly simplify model structure. The proposed technique is illustrated by solving a practical problem of failure rate model building for semiconductor production equipment based on real data.

Вісники НТУ "ХПІ"

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