In view of data mining for the decision-making of the inland waterway transportation, we study a rough set based approach to extract the decision rules with historical maritime accidents data in the inland rivers. In order to solve the NP-hard problem of attribute reduction in Rough Set Theory, the genetic algorithm based attribute reduction is proposed and described in detailed steps. Noting t...

In this paper, concepts of knowledge entropy and knowledge entropy-based uncertainty measures are given in incomplete information systems and decision systems, and some important properties of them are investigated. From these properties, it can be shown that these measures provide important approaches to measure the uncertainty ability of different knowledge in incomplete decision systems. The...

Pruning algorit hms for feed-forward neur al networks typically have the undesirable side effect of int erfering with t he learning pro cedure. The network reduct ion algorithm presented in this pap er is implemented by considering only directions in weight space that are orthogonal to those required by t he learning algorit hm. In this way, the network redu ction algorithm chooses a minimal ne...

Knowledge reduction in rough set theory is an important feature selection method. Since it is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, to address this issue, by introducing rough entropy in information systems, the novel measures of conditional rough entropy with distinguishing consistent objects form inconsistent objects are p...

0377-2217/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.ejor.2010.08.017 ⇑ Corresponding author. Tel.: +886 2 27883799. E-mail addresses: [email protected] (T.-F. Fan), Liau), [email protected] (D.-R. Liu). Attribute reduction is very important in rough set-based data analysis (RSDA) because it can be used to simplify the induced decision rules without reducing the classification accu...

Abstract Utilizing Artificial Intelligence (AI) techniques to forecast, recognize, and classify financial crisis roots are important research challenges that have attracted the interest of researchers. Moreover, Explainable (XAI) concept enables AI interpret results processing testing complex data patterns so humans can find efficient ways infer logic behind classifying patterns. This paper pro...

Knowledge reduction, includes attribute reduction and value reduction, is an important topic in rough set literature. It is also closely relevant to other fields, such as machine learning and data mining. In this paper, an algorithm called TWI-SQUEEZE is proposed. It is so named because it can find a reduct, or an irreducible attribute subset that maintains certainty of classification, after tw...

OBJECTIVE To present a quick algorithm to automatically analyze the raw data acquired by a photo-oculography (POG) system. METHODS We developed a simple algorithm for POG data analysis based on an extrapolation of missing values due to blinking and on exclusion of outliers using the robust mean and standard deviation. RESULTS POG curves of 4 children aged between 1.5 and 7 years are shown b...

We examine a probabilistic model for the diagnosis of multiple diseases. In the model, diseases and findings are represented as binary variables. Also, diseases are marginally independent, features are conditionally independent given disease instances, and diseases interact to produce findings via a noisy or-gate. An algorithm for computing the posterior probability of each disease, given a set...

  We relax some assumptions of the traditional scheduling problem and suggest an adapted meta-heuristic algorithm to optimize efficient utilization of resources and quick response to demands simultaneously. We intend to bridge the existing gap between theory and real industrial scheduling assumptions (e.g., hot metal rolling industry, chemical and pharmaceutical industries). We adapt and evalua...