Technological advances and innovations have led to various pre- post-disaster data collection alternatives. Hence, selecting a suitable survey approach may be challenging for different decision-makers. This paper proposes multicriteria decision-making (MCDM) method choose the optimal gather exposure information needed reliable multi-hazard risk assessment of large building infrastructure portfo...

In this paper, Genetic Algorithm (GA) is used to search for combinations of learning algorithms and associated parameters with maximum accuracy. An important feature of the approach is that the GA initial population is formed by using parameter values gathered from ExpDB (a public database of data mining experiments). The proposed approach was implemented in a tool called EMiner, built on top o...

This article deals with the group interview problem, in which each group contains several alternatives and each group of alternatives is presented and evaluated sequentially over time. We derive the optimal selection strategy for the group interview problem with a general utility function. Among the various types of utility function, we focus on the best choice problem, in which our utility is ...

With the development of storage technique, new caching policies are continuously being introduced, which makes it increasingly important for cache systems to select the optimal caching policy dynamically under varying workloads. This paper proposes SOPA, a mechanism to adaptively select the optimal policy and perform policy switching. First, SOPA encapsulates the functions of a caching policy i...

In the current research, we aim to define a new form of the similarity coefficient to compare the resemblance grade of two Gaussian density functions. We aim to assess the method utility on a theoretical model. The density functions are stated for a biological marker “survival length”, observed in three groups of patients, suffering from a hypothetical disease. The first group consists of patie...

In this paper, we conduct a quantitative analysis for a strategic risk management problem that involves allocating certain available failure-mitigating and consequence-alleviating resources to reduce the failure risk of system safety components and subsequent losses, respectively , together with selecting optimal strategic decision alternatives, in order to minimize the risk or expected loss in...

We study a novel machine learning (ML) problem setting of sequentially allocating smallsubsets of training data amongst a large set of classifiers. The goal is to select a classifierthat will give near-optimal accuracy when trained on all data, while also minimizing the costof misallocated samples. This is motivated by large modern datasets and ML toolkits withmany combinations ...

This paper presents expressions for the optimal step length to use when training a vector quantizer by stochastic approximation. By treating each update as an estimation problem, it provides a unified framework covering both batch and incremental training, which were previously treated separately, and extends existing results to the semibatch case. In addition, the new results presented here pr...