James N. K. Liu

Application of decision-making techniques in supplier selection: A systematic review of literature

Despite the importance of decision-making (DM) techniques for construction of effective decision models for supplier selection, there is a lack of a systematic literature review for it. This paper provides a systematic literature review on articles published from 2008 to 2012 on the application of DM techniques for supplier selection. By using a methodological decision analysis in four aspects including decision problems, decision makers, decision environments, and decision approaches, we finally selected and reviewed 123 journal articles. To examine the research trend on uncertain supplier selection, these articles are roughly classified into seven categories according to different uncertainties. Under such classification framework, 26 DM techniques are identified from three perspectives: (1) Multicriteria decision making (MCDM) techniques, (2) Mathematical programming (MP) techniques, and (3) Artificial intelligence (AI) techniques. We reviewed each of the 26 techniques and analyzed the means of integrating these techniques for supplier selection. Our survey provides the recommendation for future research and facilitates knowledge accumulation and creation concerning the application of DM techniques in supplier selection.

Gait flow image

In this paper, we propose a novel gait representation-gait flow image (GFI) for use in gait recognition. This representation will further improve recognition rates. The basis of GFI is the binary silhouette sequence. GFI is generated by using an optical flow field without constructing any model. The performance of the proposed representation was evaluated and compared with the other representations, such as gait energy image (GEI), experimentally on the USF data set. The USF data set is a public data set in which the image sequences were captured outdoors. The experimental results show that the proposed representation is efficient for human identification. The average recognition rate of GFI is better than that of the other representations in direct matching and dimensional reduction approaches. In the direct matching approach, GFI achieved an average identification rate 42.83%, which is better than GEI by 3.75%. In the dimensional reduction approach, GFI achieved an average identification rate 43.08%, which is better than GEI by 1.5%. The experimental result showed that GFI is stronger in resisting the difference of the carrying condition compared with other gait representations.

OWA operator based link prediction ensemble for social network

This paper firstly studied the link prediction ensemble for local information based algorithms.The integration of individual algorithms is finished via OWA operator.Experimental results reveal the better performances of our proposed link prediction ensemble algorithm. The objective of link prediction for social network is to estimate the likelihood that a link exists between two nodes. Although there are many local information-based algorithms which have been proposed to handle this essential problem in the social network analysis, the empirical observations show that the stability of local information-based algorithm is usually very low, i.e., the variabilities of local information-based algorithms are high. Thus, motivated by obtaining a stable link predictor with low variance, this paper proposes a kind of ordered weighted averaging (OWA) operator based link prediction ensemble algorithm (LPEOWA) for social network by assigning the aggregation weights for nine local information-based link prediction algorithms with three different OWA operators. The finally experimental results on benchmark social network datasets show that LPEOWA obtains a more stable prediction performance and considerably improves the prediction accuracy which is measured by the area under the receiver operating characteristic curve (AUC) in comparison with nine individual prediction algorithms.

Tropical cyclone identification and tracking system using integrated neural oscillatory elastic graph matching and hybrid RBF network track mining techniques

In this paper, we present an automatic and integrated neural network-based tropical cyclone (TC) identification and track mining system. The proposed system consists of two main modules: 1) TC pattern identification system using neural oscillatory elastic graph matching model (NOEGM); and 2) TC track mining system using hybrid radial basis function (HRBF) network with time difference and structural learning (TDSL) algorithm.For system evaluation, 120 TC cases appeared in the period between 1985 and 1998 provided by National Oceanic and Atmospheric Administration (NOAA) are being used. In TC pattern recognition from satellite pictures, an overall 98% of correct TC pattern segmentation rate and over 97% of correct classification rate are attained. Moreover, for TC track and intensity mining test, promising result of over 86% is achieved with the application of the hybrid RBF network. Comparing with the bureau numerical TC prediction model (OTCM) used by Guam and the enhanced model (TFS) proposed by Jeng et al., the proposed hybrid RBF has attained an over 30% and 18% improvement in forecast errors.

Inter-transactional association rules for multi-dimensional contexts for prediction and their application to studying meterological data

Inter-transactional association rules, first presented in our early work [H. Lu, J. Han, L. Feng, Stock movement prediction and n-dimensional inter-transaction association rules, in: Proceedings of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Seattle, Washington, June 1998, pp. 12:1–12:7; H. Lu, L. Feng, J. Han, ACM Trans. Inf. Syst. 18 (4) (2000) 423–454], give a more general view of association relationships among items. Two kinds of algorithms, named Extended/Extended Hash-based Apriori (E/EH-Apriori) [Lu et al. (1998, 2000), loc. cit.] and First-Intra-Then-Inter (FITI) [K. H. Tung, H. Lu, J. Han, L. Feng, Breaking the barrier of transactions: Mining inter-transaction association rules, in: Proceedings ACM SIGKDD International Conference Knowledge Discovery and Data Mining, USA, August 1999, pp. 297–301], were presented for mining inter-transactional association rules from large data sets. A template-guided constraint-based inter-transactional association mining method was described in [L. Feng, H. Lu, J. Yu, J. Han, Mining inter-transaction association rules with templates, in: Proceedings ACM CIKM International Conference Information and Knowledge Management, USA, November 1999, pp. 225–233]. The current paper extends our previous work substantially in both theoretical and practical aspects. In the theoretical aspects, we improve the inter-transactional association rule framework by giving a more concise definition of inter-transactional association rules and related measurements, and investigate the closure property, theoretical foundations, multi-dimensional mining contexts, and performance issues in mining such extended association rules. We study the downward closure property problem within the inter-transactional association rule framework, and propose a solution for efficient mining of inter-transactional association rules. A set of examples, lemmas and theorems are provided to verify our discussions. We also present a hole-catered extended Apriori algorithm for mining inter-transactional association rules. Different from our previous work, here, we take data holes that possibly exist in the mining contexts into consideration. We also address some important technical issues, including correctness, termination and computational complexity, in this paper. In practice, we study the applicability of inter-transactional association rules to weather prediction, using multi-station meteorological data obtained from the Hong Kong Observatory headquarters. We report our experimental results as well as the experiences gained during the weather study. In particular, the deficiency of the current support/confidence-based association mining framework and its further extension in providing multi-dimensional predictive capabilities are addressed. These extensions significantly augment the theory and practicality of the more general inter-transactional association rules. It is our hope that the work reported here could stimulate further interest not only in the applications of association rule techniques to non-transactional real-world data under multi-dimensional contexts, but also in the relevant theoretical and performance issues of association rule techniques.

A rough set-based case-based reasoner for text categorization

This paper presents a novel rough set-based case-based reasoner for use in text categorization (TC). The reasoner has four main components: feature term extractor, document representor, case selector, and case retriever. It operates by first reducing the number of feature terms in the documents using the rough set technique. Then, the number of documents is reduced using a new document selection approach based on the case-based reasoning (CBR) concepts of coverage and reachability. As a result, both the number of feature terms and documents are reduced with only minimal loss of information. Finally, this smaller set of documents with fewer feature terms is used in TC. The proposed rough set-based case-based reasoner was tested on the Reuters21578 text datasets. The experimental results demonstrate its effectiveness and efficiency as it significantly reduced feature terms and documents, important for improving the efficiency of TC, while preserving and even improving classification accuracy.

iJADE Web-miner: an intelligent agent framework for Internet shopping

There is growing interest in using intelligent software agents for a variety of tasks, including navigating and retrieving information from the Internet and from databases, online shopping activities, user authentication, negotiation for resources, and decision making. We propose an integrated framework for information retrieval and information filtering in the context of Internet shopping. We focus on applying agent technology, together with Web mining technology, to automate a series of product search and selection activities. It is based on a multiagent development platform, namely, iJADE (intelligent Java agent development environment), which supports various e-commerce applications. The framework comprises an automatic facial authentication utility and six other modules, namely, customer requirements definition, a requirement-fuzzification scheme, a fuzzy agents-negotiation scheme, a fuzzy product-selection scheme, a product-defuzzification scheme, and a product-evaluation scheme. A series of experiments were carried out and favorable results were produced in executing the framework. From an experimental point of view, we used a database of 1,020 facial images that were obtained under various conditions of facial expression, viewing perspective and size. An overall correct recognition rate of over 85 percent was attained. For the product selection test of our fuzzy shopper system, an average matching rate of more than 81 percent was achieved.

A NEW RULE-BASED SIR APPROACH TO SUPPLIER SELECTION UNDER INTUITIONISTIC FUZZY ENVIRONMENTS

Multiple Criteria Decision Making (MCDM) aims at giving people a knowledge recommendation concerning a set of objects evaluated from multiple preference-ordered attributes. The Superiority and Inferiority Ranking (SIR) is a generation of the well-known outranking approach-PROMETHEE, which is an efficient approach for MCDM. As the traditional MCDM approach, however, it faces the obstacle in handling uncertainties of real world. We are concerned about the issue on how to extend the traditional MCDM approach for applications in uncertain environments. This paper proposes a new Intuitionistic Fuzzy SIR (IF-SIR for short) approach and focuses on its application to supplier selection which is the important activity in supply chain management. Toward practical applications, two factors are considered here: (1) multiple decision makers and (2) decision information in the form of linguistic terms. We firstly identify these terms via Intuitionistic Fuzzy Set (IFS) which is proven to be a powerful mathematical tool in modeling uncertain information. Then, we provide the IF-SIR approach for group aggregation and decision analysis. Hereinto, a rule-based method is developed for ranking and selection of suppliers. Finally, an illustrative example is used for illustration of the proposed approach.

Automatic extraction and identification of chart patterns towards financial forecast

Technical analysis of stocks mainly focuses on the study of irregularities, which is a non-trivial task. Because one time scale alone cannot be applied to all analytical processes, the identification of typical patterns on a stock requires considerable knowledge and experience of the stock market. It is also important for predicting stock market trends and turns. The last two decades has seen attempts to solve such non-linear financial forecasting problems using AI technologies such as neural networks, fuzzy logic, genetic algorithms and expert systems but these, although promising, lack explanatory power or are dependent on domain experts. This paper presents an algorithm, PXtract to automate the recognition process of possible irregularities underlying the time series of stock data. It makes dynamic use of different time windows, and exploits the potential of wavelet multi-resolution analysis and radial basis function neural networks for the matching and identification of these irregularities. The study provides rooms for case establishment and interpretation, which are both important in investment decision making.

A novel believable rough set approach for supplier selection

We consider the issue of supplier selection by using rule-based methodology. Supplier Selection (SS) is an important activity in Logistics and Supply Chain Management in todays global market. It is one of major applications of Multiple Criteria Decision Analysis (MCDA) that concerns about preference-related decision information. The rule-based methodology is proven of its effectiveness in handling preference information and performs well in sorting or ranking alternatives. However, how to utilize them in SS still remains open for more studies. In this paper, we propose a novel Believable Rough Set Approach (BRSA). This approach performs the complete problem-solving procedures including (1) criteria analysis, (2) rough approximation, (3) decision rule induction, and (4) a scheme for rule application. Unlike other rule-based solutions that just extract certain information, the proposed solution additionally extracts valuable uncertain information for rule induction. Due to such mechanism, BRSA outperforms other solutions in evaluation of suppliers. A detailed empirical study is provided for demonstration of decision-making procedures and multiple comparisons with other proposals.