This study focuses on mechanism design in order to solve multi-attribute e-procurement problems. In particularly, this study addresses two realistic requirements in mechanism design: (1) specifications of request/proposal on multiple attributes, and (2) incentive compatibility on information exchange/disclosure. Taking into account the needs and emergence of advanced mechanisms in eprocurement,...

Interval judgments are a way of handling preferential and informational uncertainty in multicriteria decision analysis. In this paper, we study the use of intervals in SMART and SWING weighting methods. We generalize the methods in three ways: (i) the reference attribute is allowed to be any attribute, not just the most or least important one, (ii) the decision maker can reply with intervals to...

Integrating phonetic knowledge into a speech recognizer is a possible way to further improve the performance of conventional HMM-based speech recognition methods. This paper presents a cascaded architecture which consists of attribute detection and conditional random field to make use of phonetic knowledge within the phone decoding process. The attribute detection can be implemented by using an...

Using evolutionary algorithms, a search is performed based on a population where each population member consists of a vector of attribute values and a fitness value. A simulation of a system is run, given a particular set of the member attribute values, producing a fitness value. Fitness measures how well the system achieves its mission objectives. If the fitness has a random component, several...

Developing robust and less complex models capable of coping with environment volatility is the quest of every data mining project. This study attempts to establish heuristics for investigating the impact of noise in instance attributes data on learning model volatility. In addition, an alternative method for determining attribute importance and feature ranking, based on attribute sensitivity to...

10 This article proposes a framework to handle multiattribute group decision making 11 problems with incomplete pairwise comparison preference over decision alternatives 12 where qualitative and quantitative attribute values are furnished as linguistic variables 13 and crisp numbers, respectively. Attribute assessments are then converted to interval14 valued intuitionistic fuzzy numbers (IVIFNs...

In order to have a software architecture design method that achieves quality attribute requirements several aspects of the method must be in place. First there must be some way to specify quality attribute requirements so that it can be determined whether the designed architecture can achieve them. Secondly, there must be some way for modularising the knowledge associated with quality attribute...

Data engineering is generally considered to be a central issue in the development of data mining applications. The success of many learning schemes, in their attempts to construct models of data, hinges on the reliable identification of a small set of highly predictive attributes. The inclusion of irrelevant, redundant and noisy attributes in the model building process phase can result in poor ...

When there are multiple competing objectives in a decision-making process, Multi-Attribute Choice scoring models are excellent tools, permitting the incorporation of both subjective and objective attributes. However, their accuracy depends upon the subjective techniques used to construct the attribute scales and their concomitant weights. Conventional techniques using local scales tend to overe...

We address the problem of learning qualitative relations in categorical domains. We propose an algorithm that observes the change of probability of a target class w.r.t. the change in the values of the selected attribute for each learning example. We generalize the notion of a partial derivative by defining the probabilistic discrete qualitative partial derivative (PDQ PD). PDQ PD is a qualitat...