Learning a fuzzy decision tree from uncertain data

© 2017 IEEE. Uncertainty in data exists when the value of a data item is not a precise value, but rather by an interval data with a probability distribution function, or a probability distribution of multiple values. Since there are intrinsic differences between uncertain and certain data, it is difficult to deal with uncertain data using traditional classification algorithms. Therefore, in this paper, we propose a fuzzy decision tree algorithm based on a classical ID3 algorithm, it integrates fuzzy set theory and ID3 to overcome the uncertain data classification problem. Besides, we propose a discretization algorithm that enables our proposed Fuzzy-ID3 algorithm to handle the interval data. Experimental results show that our Fuzzy-ID3 algorithm is a practical and robust solution to the problem of uncertain data classification and that it performs better than some of the existing algorithms

Data-Driven Dynamic Modeling for Prediction of Molten Iron Silicon Content Using ELM with Self-Feedback

Silicon content ([Si] for short) of the molten metal is an important index reflecting the product quality and thermal status of the blast furnace (BF) ironmaking process. Since the online detection of [Si] is difficult and larger time delay exists in the offline assay procedure, quality modeling is required to achieve online estimation of [Si]. Focusing on this problem, a data-driven dynamic modeling method is proposed using improved extreme learning machine (ELM) with the help of principle component analysis (PCA). First, data-driven PCA is introduced to pick out the most pivotal variables from multitudinous factors to serve as the secondary variables of modeling. Second, a novel data-driven ELM modeling technology with good generalization performance and nonlinear mapping capability is presented by applying a self-feedback structure on traditional ELM. The feedback outputs at previous time together with input variables at different time constitute a dynamic ELM structure which has a storage ability to tackle data in different time and overcomes the limitation of static modeling of traditional ELM. At last, industrial experiments demonstrate that the proposed method has a better modeling and estimating accuracy as well as a faster learning speed when compared with different modeling methods with different model structures

A Data Mining Methodology for Vehicle Crashworthiness Design

This study develops a systematic design methodology based on data mining theory for decision-making in the development of crashworthy vehicles. The new data mining methodology allows the exploration of a large crash simulation dataset to discover the underlying relationships among vehicle crash responses and design variables at multiple levels and to derive design rules based on the whole-vehicle safety requirements to make decisions about component-level and subcomponent-level design. The method can resolve a major issue with existing design approaches related to vehicle crashworthiness: that is, limited abilities to explore information from large datasets, which may hamper decision-making in the design processes. At the component level, two structural design approaches were implemented for detailed component design with the data mining method: namely, a dimension-based approach and a node-based approach to handle structures with regular and irregular shapes, respectively. These two approaches were used to design a thin-walled vehicular structure, the S-shaped beam, against crash loading. A large number of design alternatives were created, and their responses under loading were evaluated by finite element simulations. The design variables and computed responses formed a large design dataset. This dataset was then mined to build a decision tree. Based on the decision tree, the interrelationships among the design parameters were revealed, and design rules were generated to produce a set of good designs. After the data mining, the critical design parameters were identified and the design space was reduced, which can simplify the design process. To partially replace the expensive finite element simulations, a surrogate model was used to model the relationships between design variables and response. Four machine learning algorithms, which can be used for surrogate model development, were compared. Based on the results, Gaussian process regression was determined to be the most suitable technique in the present scenario, and an optimization process was developed to tune the algorithm’s hyperparameters, which govern the model structure and training process. To account for engineering uncertainty in the data mining method, a new decision tree for uncertain data was proposed based on the joint probability in uncertain spaces, and it was implemented to again design the S-beam structure. The findings show that the new decision tree can produce effective decision-making rules for engineering design under uncertainty. To evaluate the new approaches developed in this work, a comprehensive case study was conducted by designing a vehicle system against the frontal crash. A publicly available vehicle model was simplified and validated. Using the newly developed approaches, new component designs in this vehicle were generated and integrated back into the vehicle model so their crash behavior could be simulated. Based on the simulation results, one can conclude that the designs with the new method can outperform the original design in terms of measures of mass, intrusion and peak acceleration. Therefore, the performance of the new design methodology has been confirmed. The current study demonstrates that the new data mining method can be used in vehicle crashworthiness design, and it has the potential to be applied to other complex engineering systems with a large amount of design data