This empirical study is mainly devoted to comparing four tree-based boosting algorithms: mart, abc-mart, robust logitboost, and abc-logitboost, for multi-class classification on a variety of publicly available datasets. Some of those datasets have been thoroughly tested in prior studies using a broad range of classification algorithms including SVM, neural nets, and deep learning. In terms of t...

multi-echelon inventory systems are one of the most important and attractive areas in modelling supply chain management so that several researches and studies have been done about. in this paper, the total cost function of a two-echelon inventory system with central warehouse and many identical retailers controlled by continuous review inventory policy is estimated. we have assumed the demand i...

Original scientific paper Inventory classification is a very important part of inventory control which represents the technique of operational research discipline. A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. The paper describes the results obtained by investigating the application of neural networks in multiple c...

To obtain competitive advantages in the global market scenario, companies have aimed at reducing logistic costs as well as streamlining the order fulfillment. The use of tools to efficiently manage the warehouse, ensuring a best allocation of the products has been growing. The method of the ABC Curve, based on the Principle of Pareto qualifies the portfolio of a company so that products can be ...

We develop abc-logitboost, based on the prior work on abc-boost[10] and robust logitboost[11]. Our extensive experiments on a variety of datasets demonstrate the considerable improvement of abc-logitboost over logitboost and abc-mart.

this paper presents a new strategy based on multi-stage fuzzy (msf) pid controller based on artificial bee colony (abc) for damping power system stabilizer (pss) in multi-machine environment. the recent studies in artificial intelligence demonstrated that the abc optimization is strong intelligent method in complicated stability problems. also, finding the parameters of pid controller in power ...

Abc-boost is a new line of boosting algorithms for multi-class classification, by utilizing the commonly used sum-to-zero constraint. To implement abc-boost, a base class must be identified at each boosting step. Prior studies used a very expensive procedure based on exhaustive search for determining the base class at each boosting step. Good testing performance of abc-boost (implemented as abc...