4 research outputs found
Искусственная нейронная сеть для нелинейной декомпозиции функций
Запропоновано нову архiтектуру штучної нейронної мережi для розв’язання задачi нелiнiйної декомпозицiї функцiй. Використано спадний пiдхiд, що не потребує апрiорної iнформацiї про властивостi аналiзовної функцiї. Можливостi запропонованого методу продемонстрованi на синтетичних тестових функцiях i пiдтвердженi розв’язанням реальної задачi.A novel neural network architecture is proposed to solve the nonlinear function decomposition problem. The top-down approach that does not require an a priori knowledge about the function’s properties is applied. Abilities of the proposed method are demonstrated using synthetic test functions and confirmed by solving a real problem
Data complexity in machine learning
We investigate the role of data complexity in the context of binary classification problems. The universal data complexity is defined for a data set as the Kolmogorov complexity of the mapping enforced by the data set. It is closely related to several existing principles used in machine learning such as Occam's razor, the minimum description length, and the Bayesian approach. The data complexity can also be defined based on a learning model, which is more realistic for applications. We demonstrate the application of the data complexity in two learning problems, data decomposition and data pruning. In data decomposition, we illustrate that a data set is best approximated by its principal subsets which are Pareto optimal with respect to the complexity and the set size. In data pruning, we show that outliers usually have high complexity contributions, and propose methods for estimating the complexity contribution. Since in practice we have to approximate the ideal data complexity measures, we also discuss the impact of such approximations
Feature construction using explanations of individual predictions
Feature construction can contribute to comprehensibility and performance of
machine learning models. Unfortunately, it usually requires exhaustive search
in the attribute space or time-consuming human involvement to generate
meaningful features. We propose a novel heuristic approach for reducing the
search space based on aggregation of instance-based explanations of predictive
models. The proposed Explainable Feature Construction (EFC) methodology
identifies groups of co-occurring attributes exposed by popular explanation
methods, such as IME and SHAP. We empirically show that reducing the search to
these groups significantly reduces the time of feature construction using
logical, relational, Cartesian, numerical, and threshold num-of-N and X-of-N
constructive operators. An analysis on 10 transparent synthetic datasets shows
that EFC effectively identifies informative groups of attributes and constructs
relevant features. Using 30 real-world classification datasets, we show
significant improvements in classification accuracy for several classifiers and
demonstrate the feasibility of the proposed feature construction even for large
datasets. Finally, EFC generated interpretable features on a real-world problem
from the financial industry, which were confirmed by a domain expert.Comment: 54 pages, 10 figures, 22 table