Fuzzy rough sets (FRSs) are considered to be a powerful model for analyzing uncertainty in data. This model encapsulates two types of uncertainty: 1) fuzziness coming from the vagueness in human concept formation and 2) roughness rooted in the granulation coming with human cognition. The rough set theory has been widely applied to feature selection, attribute reduction, and classification. However, it is reported that the classical FRS model is sensitive to noisy information. To address this problem, several robust models have been developed in recent years. Nevertheless, these models do not consider a statistical distribution of data, which is an important type of uncertainty. Data distribution serves as crucial information for designing an optimal classification or regression model. Thus, we propose a data-distribution-aware FRS model that considers distribution information and incorporates it in computing lower and upper fuzzy approximations. The proposed model considers not only the similarity between samples, but also the probability density of classes. In order to demonstrate the effectiveness of the proposed model, we design a new sample evaluation index for prototype-based classification based on the model, and a prototype selection algorithm is developed using this index. Furthermore, a robust classification algorithm is constructed with prototype covering and nearest neighbor classification. Experimental results confirm the robustness and effectiveness of the proposed model.
Software Engineering is inherently knowledge intensive. Software processes and products are human centered. The technology of Computational Intelligence (CI) intensively exploits various mechanisms of interaction with humans and processes domain knowledge with intent of building intelligent systems. As commonly perceived, CI dwells on three highly synergistic technologies of neural networks, fuzzy sets (or granular computing, in general) and evolutionary optimization. As the software complexity grows and the diversity of software systems skyrocket, it becomes apparent that there is a genuine need for a solid, efficient, designer-oriented vehicle to support software analysis, design, and implementation at various levels. The research agenda makes CI a highly compatible and appealing vehicle to address the needs of knowledge rich environment of Software Engineering. The objective of this study is to identify and discuss synergistic links emerging between Software Engineering and Computational Intelligence. We show how CI --- based models contribute to the methodology of constructing models of software processes and products. Several selected examples (including software cost estimation, quality, and software measures) are included.
In this study, we propose linguistic descriptors-based approach to the problem of face identification realized by both humans and computers. This approach is motivated by an evident observation that linguistic descriptors offer an ability to formalize and exploit important pieces of knowledge describing human's face. These entities are used by people in face recognition and could be found of importance in building machine-oriented recognition schemes. Moreover, evident humans' abilities to recognize other individuals can be incorporated into computational face recognition problems as an invaluable vehicle improving recognition rate of machine-oriented classifiers. Specifically, we propose an application of analytic hierarchy process to determine linguistic values of facial features. The experts' assessments of faces in terms of such attributes support coping with uncertainty captured through experts' decisions result in a set of useful assuring the desired property of inter-class similarities and between-class differences among faces. It is worth noting that the method presented in this study can be easily applied to any other classification problem with the presence of experts.
In his paper, we introduce a model of generalization and specialization of information granules. The information granules themselves are modeled as fuzzy sets or fuzzy relations. The generalization is realized by or-ing fuzzy sets while the specialization is completed through logic and operation. These two logic operators are realized using triangular norms (that is t- and a-norms). We elaborate on two (top-down and bottom-up) strategies of constructing information granules that arise as results of generalization and specialization. Various triangular norms are experimented with and some conclusions based on numeric studies are derived.
Advances in data collection and storage capabilities during the past decades have led to an information overload in most application domains. The huge amount of data the real-world applications has necessitated the use of a reduction mechanism. The reduction method contains two main techniques: feature selection and instance selection, which are usually applied individually. Although, some work has been done to implement the feature and instance selection simultaneously, this work has focused on mainly the classification problem. This paper proposes the integration of feature selection and instance selection for solving the regression problem by using the fuzzy modeling approach. The selection of features and instances is based on the cooperative particle swarm optimization technique, which aims to limit the effect of the curse of dimensionality that occurs when dealing with the high dimensionality of the search space. The proposed method is applied to three real-world datasets from the machine learning repository. The algorithm's performance is illustrated by the corresponding plots of the prediction error for the different amounts of data being selected.
The coronavirus D-19 (Covid-19) pandemic has shaken almost every country in the world: as we stand, 6,3 million deaths from the infection have already been recorded, 167,000 and 380,000 of which are in Italy and the Russian Federation, respectively. In the first wave of the pandemic, Italy suffered an abnormally high death toll. A detailed analysis of available epidemiological data suggests that that rate was shockingly high in the Northern regions and in Lombardy, in particular, whilst in the southern region the situation was less dire. This inexplicably high mortality rate in conditions of a very well-developed health care system such as the one in Lombardy recognized as one of the best in Italy certainly cries for a convincing explanation. In 1976, the small city of Seveso, Lombardy, experienced a release of dioxin into the atmosphere after a massive technogenic accident. The immediate effects of the industrial disaster did not become apparent until a surge in the number of tumors in the affected population in the subsequent years. In this paper, we endeavor to prove our hypothesis that the release of dioxin was a negative cofactor that contributed to a worsening of the clinical course of COVID-19 in Lombardy.
Some of the recent advancements in Artificial Intelligence (AI) fall under the umbrella of industrial developments (which are predominantly driven by numeric data) and explainable AI (XAI). We advocate that in the realization of these two timely pursuits, information granules and Granular Computing play a significant role. First, it is shown that information granularity is of paramount relevance in building linkages between real-world data and symbols commonly encountered in AI processing. Second, we stress that a suitable level of abstraction (specificity of information granularity) becomes essential to support user-oriented framework of design and functioning AI artifacts. In both cases, central to all pursuits is a process of formation of information granules and their prudent characterization. We discuss a comprehensive approach to the development of information granules by means of the principle of justifiable granularity. Here various construction scenarios are discussed including those engaging conditioning and collaborative mechanisms incorporated in the design of information granules. The mechanisms of assessing the quality of granules are presented. In the sequel, we look at the generative and discriminative aspects of information granules supporting their further usage in the formation of granular models. A symbolic manifestation of information granules is put forward and analyzed from the perspective of semantically sound descriptors of data and relationships among data. With this regard, selected aspects of stability and summarization of symbol-oriented information are discussed.
