Decision rules are one of the most expressive languages for machine learning. In this paper we present Adaptive Model Rules (AMRules), the first streaming rule learning algorithm for regression problems. In AMRules the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of attribute values. Each rule uses a PageHinkley test to ...

Action rules describe possible transitions of objects from one state to another with respect to a distinguished attribute. Early research on action rule discovery usually required the extraction of classification rules before constructing any action rule. Newest algorithms discover action rules directly from a decision system. To our knowledge, all these algorithms assume that all attributes ar...

The main aim of the present study is to optimize steel moment frames in the framework of performance-based design and to assess the seismic collapse capacity of the optimal structures. In the first phase of this study, four well-known metaheuristic algorithms are employed to achieve the optimization task. In the second phase, the seismic collapse safety of the obtained optimal designs is evalua...

Solving engineering problems using conventional neural networks requires long-term research on the choice of architecture and hyperparameters. A strong artificial intelligence would be devoid such shortcomings. Such is carried out a very wide range approaches: for example, biological (attempts to grow brain in laboratory conditions), hardware (creating processors) or software (using power ordin...

Urban morphology, including buildings’ typology and their configuration, affects sky view factor and insolation, which are two of the most influential parameters in urban microclimate. These become even more important in extreme environmental conditions. In this research, challenge was to generate various rule based urban morphologies in a parametric environment. Then, to evaluate them based on...

In this paper, the relation among factors in the road transportation sector from March, 2005 to March, 2011 is analyzed. Most of the previous studies have economical point of view on gasoline consumption. Here, a new approach is proposed in which different data mining techniques are used to extract meaningful relations between the aforementioned factors. The main and dependent factor is gasolin...

anomaly detection (ad) has recently become an important application of hyperspectral images analysis. the goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. one way to improve the performance and runtime of these algorithms is to use dimensionality reduction (dr) techniques. this paper evaluates the effect of thr...