An interpretable neural fuzzy inference system for predictions of underpricing in initial public offerings

Wang, Di; Qian, Xiaolin; Quek, Chai; Tan, Ah-Hwee; Miao, Chunyan; Zhang, Xiaofeng; Ng, Geok See; Zhou, You

doi:10.1016/j.neucom.2018.07.036

Cited by 17 publications

(8 citation statements)

References 57 publications

(76 reference statements)

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“…To efficiently deal with large-scale instances, heuristic algorithms are often applied to obtain "sub-optimal solutions" or "satisfactory solutions". At present, the most commonly adopted heuristic algorithms are Simulated Annealing (SA) [33], Genetic Algorithm (GA) [34], Tabu Search (TS) [35], Ant colony optimization (ACO) [36], Artificial Neural Network (ANN) [37], Particle Swarm Optimization (PSO) [38], etc.…”

Section: Related Workmentioning

confidence: 99%

Goods Consumed During Transit in Split Delivery Vehicle Routing Problems: Modeling and Solution

Yang¹,

Wang²,

Pang³

et al. 2020

IEEE Access

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This article presents the modeling and solution of an extended type of split delivery vehicle routing problem (SDVRP). In SDVRP, the demands of customers need to be met by efficiently routing a given number of capacitated vehicles, wherein each customer may be served multiple times by more than one vehicle. Furthermore, in many real-world scenarios, consumption of vehicles en route is the same as the goods being delivered to customers, such as food, water and fuel in rescue or replenishment missions in harsh environments. Moreover, the consumption may also be in virtual forms, such as time spent in constrained tasks. We name such a real-world SDVRP as Split Delivery Vehicle Routing Problem with Goods Consumed during Transit (SDVRP-GCT). In this paper, we give mathematical formulas to model SDVRP-GCT and provide solutions by extending three ant colony algorithms. To the best of our knowledge, this is the first research work specifically focussing on the SDVRP-GCT problem and its solutions. To assess the effectiveness of our proposed ant colony algorithms, we first apply them on widely adopted SDVRP benchmarking instances on different scales and their correspondingly extended SDVRP-GCT instances. Then, we formulate a real-world SDVRP-GCT instance for further assessment. Based on the extensive experimental results, we discuss the pros and cons of our proposed solutions and subsequently suggest their preferable application scenarios. In summary, our proposed solutions are shown as highly efficient in solving SDVRP-GCT instances.

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Section: Related Workmentioning

confidence: 99%

Goods Consumed During Transit in Split Delivery Vehicle Routing Problems: Modeling and Solution

Yang¹,

Wang²,

Pang³

et al. 2020

IEEE Access

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“…Clustering methods have been widely applied to discover the intrinsic patterns embedded in various type of data, such as data analytic in general [14,20], financial predictions [19,22], lifestyle differentiation [24], disease diagnosis [21], etc. In this paper, we investigate the public transportation data collected in Chicago, USA, across the whole year of 2016.…”

Section: Analysis Of Shared Bike Usagementioning

confidence: 99%

Analysis of Public Transportation Patterns in a Densely Populated City with Station-based Shared Bikes

Wang

Tan

2018

Proceedings of the 3rd International Conference on Crowd Science and Engineering

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Densely populated cities face great challenges of high transportation demand and limited physical space. Thus, in these cities, the public transportation system is heavily relied on. Conventional public transportation modes such as bus, taxi and subway have been globally deployed over the past century. In the last decade, a new type of public transportation mode, shared bike, emerged in many cities. These shared bikes are deployed by either government-regulated or profit-driven companies and are either station-based or station-less. Nonetheless, all of them are designed to better solve the last-mile problem in densely populated cities as complements to the conventional public transportation system. In this paper, we analyse the public transportation patterns in a densely populated city, Chicago, USA, using comprehensive datasets covering the transportation records on shared bikes, buses, taxis and subways collected over one year's time. Specifically, we apply self-regulated clustering methods to reveal both the majority transportation patterns and the irregular ones. Other than reporting the autonomously discovered transportation patterns, we also show that our method achieves better clustering performance than the benchmarking methods.

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“…This method could be used effectively in other workplaces, such as hospitals and health-care facilities. Wang et al [9] developed the Genetic Algorithm and Rough Set Incorporated Neural Fuzzy Inference System (GARSINFIS), which is an integrated autonomous computational model for underpricing forecasting in initial public offerings. The experimental results showed a higher yield of initial returns for initial public offerings, by following the advice provided by GARSINFIS in comparison to any other benchmarking model.…”

mentioning

confidence: 99%

A Parameterized Intuitionistic Type-2 Fuzzy Inference System with Particle Swarm Optimization

Lin

Liu

et al. 2020

Symmetry

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The aim of this study was to develop a novel intuitionistic Type-2 fuzzy inference system (IT-2 FIS) which adopts a parameterized Yager-generating function and particle swarm optimization (PSO). In IT-2 FIS, the intuitionistic Type-2 is set as a fuzzy symmetrical triangular number in which the hesitation degree adopts the Yager-generating function, and the parameters of the proposed IT-2 FIS adopting the PSO are tuned. The intuitionistic and Type-2 fuzzy sets have been proven to be the most effective for handling more uncertainty. Therefore, this study proposes an intuitionistic Type-2 set with a Yager-generating function to enhance the conventional fuzzy inference system. Moreover, PSO can improve the fuzzy inference system by searching for the optimal parameters of IT-2 FIS. In this study, linguistic variables were represented by triangular fuzzy numbers (TFS). Two numerical examples were examined: capacity-planning and medical diagnosis problems. An approaching capacity-loadings example was used to verify that the proposed IT-2 FIS could effectively estimate the results of the capacity loadings. In the medical diagnosis problem, IT-2 FIS could obtain a higher correct rate by revealing experts’ knowledge. In both examples, the proposed IT-2 FIS provided more objective estimated values than traditional fuzzy inference systems (FIS) and Type-2 FIS.

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An interpretable neural fuzzy inference system for predictions of underpricing in initial public offerings

Cited by 17 publications

References 57 publications

Goods Consumed During Transit in Split Delivery Vehicle Routing Problems: Modeling and Solution

Goods Consumed During Transit in Split Delivery Vehicle Routing Problems: Modeling and Solution

Analysis of Public Transportation Patterns in a Densely Populated City with Station-based Shared Bikes

A Parameterized Intuitionistic Type-2 Fuzzy Inference System with Particle Swarm Optimization

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