In recent years, high-performance models have been introduced based on deep learning; however, these models do not have high interpretability to complement their high efficiency. Rule-based classifiers can be used to obtain explainable artificial intelligence. Rule-based classifiers use a labeled dataset to extract rules that express the relationships between inputs and expected outputs. Although many evolutionary and non-evolutionary algorithms have developed to solve this problem, we hypothesize that rule-based evolutionary algorithms such as the AntMiner family can provide good approximate solutions to problems that cannot be addressed efficiently using other techniques. This study proposes a novel supervised rule-based classifier for binary classification tasks and evaluates the extent to which algorithms in the AntMiner family can address this problem. First, we describe different versions of AntMiner. We then introduce the one-class AntMiner (OCAntMiner) algorithm, which can work with different imbalance ratios. Next, we evaluate these algorithms using specific synthetic datasets based on the AUPRC, AUROC, and MCC evaluation metrics and rank them based on these metrics. The results demonstrate that the OCAntMiner algorithm performs better than other versions of AntMiner in terms of the specified metrics.

Nowadays, firms are constantly looking for methodological approaches that help them to decrease the time needed for the innovation process. Among these approaches, it is worth mentioning the TRIZ-based frameworks such as the Inventive Design Methodology (IDM), where the Problem Graph method is used to formulate a problem. However, the application of IDM is time-consuming due to the construction of a complete map to clarify a problem situation. Therefore, the Inverse Problem Graph (IPG) method has been introduced within the IDM framework to enhance its agility. Nevertheless, the manual gathering of essential information, including parameters and concepts, requires effort and time. This paper integrates the neural network doc2vec and machine learning algorithms as Artificial Intelligence methods into a graphical method inspired by the IPG process. This integration can facilitate and accelerate the development of inventive solutions by extracting parameters and concepts in the inventive design process. The method has been applied to develop a new lattice structure solution in the material field.

The objective of this paper is to improve a machine learning based methodology for recognizing the features of a Generalized Physical Contradictions (GPC) before knowing the contradiction itself when the system to be improved can be described by a simulated model based on design parameters and performance parameters. The paper starts with the background about identifying contradictions from data. It focuses on physical contradiction parameters identification with quantitative data and machine learning techniques. Although previous approaches are promising, they still have several drawbacks that require to be fixed. For instance, they do not propose any metric to inform the user about the quality of the result, which depends, among others, on the sample size. These drawbacks mainly appear in case of imbalanced data or complex relation between variables. To address these issues, we first tested different feature importance variable provided by decision tree methods (with the XGBOOST library) and retain the total gain. Second, we compared the XGBOOST methods with the previous proposed SVM based approach to see which one better describes the feature importance of variables involved in a GPC. As result XGBOOST was more robust to the noise from non-important variables. Third, we defined a set of measures for helping the user to know which is the sample size required to get good results with the tested methods.

Addressee detection is one of the most fundamental tasks for seamless dialogue management and turn taking in human-agent interaction. Whereas addressee detection is implicit in dyadic interaction, it becomes a challenging task in multiparty interactions when more than two participants are involved. Existing research works employ either rule-based or statistical approaches for addressee detection. However, most of these works either have been tested on a single data set or only support a fixed number of participants. In this article, we propose a model based on generic features to predict the addressee in data sets with varying number of participants. The results tested on two different corpora show that the proposed model outperforms existing baselines.