Neural networks for classification: a survey

Zhang, G.P.

doi:10.1109/5326.897072

Cited by 1,432 publications

(619 citation statements)

References 173 publications

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“…The relationships among this combination of variables are likely to be non-linear. Thus, we foresee neural network techniques 24 to be useful in this case. These results may help researchers understand the task allocation strategy preferences by people from different backgrounds, and design decision support mechanisms which are more persuasive to different sub-populations.…”

Section: Usage Notesmentioning

confidence: 99%

A dataset of human decision-making in teamwork management

Shen

Miao

et al. 2017

Sci Data

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Today, most endeavours require teamwork by people with diverse skills and characteristics. In managing teamwork, decisions are often made under uncertainty and resource constraints. The strategies and the effectiveness of the strategies different people adopt to manage teamwork under different situations have not yet been fully explored, partially due to a lack of detailed large-scale data. In this paper, we describe a multi-faceted large-scale dataset to bridge this gap. It is derived from a game simulating complex project management processes. It presents the participants with different conditions in terms of team members’ capabilities and task characteristics for them to exhibit their decision-making strategies. The dataset contains detailed data reflecting the decision situations, decision strategies, decision outcomes, and the emotional responses of 1,144 participants from diverse backgrounds. To our knowledge, this is the first dataset simultaneously covering these four facets of decision-making. With repeated measurements, the dataset may help establish baseline variability of decision-making in teamwork management, leading to more realistic decision theoretic models and more effective decision support approaches.

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Section: Usage Notesmentioning

confidence: 99%

A dataset of human decision-making in teamwork management

Shen

Miao

et al. 2017

Sci Data

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“…The review by Zhang [24], which provides a summary of the most important advances in classification with ANNs, makes it clear that the advantages of neural networks lie in different aspects: their capability to adapt themselves to the data without any explicit specification of functional or distributional form for the underlying model; they are universal functional approximators; they represent nonlinear and flexible solutions for modeling real world complex relationships; and, finally, they are able to provide a basis for establishing classification rules and performing statistical analysis. On the other hand, different neuro-evolutionary approaches have been successfully applied to a variety of benchmark problems and real-world classification tasks [25][26][27]32].…”

Section: Neuro-evolutionary Classifiersmentioning

confidence: 99%

A Neuro-Evolutionary Approach to Electrocardiographic Signal Classification

Azzini

Dragoni

Tettamanzi

2014

Evolution, Complexity and Artificial Life

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Abstract. This chapter presents an evolutionary ANN classifier system as a heartbeat classification algorithm designed according to the rules of the PhysioNet/Computing in Cardiology Challenge 2011 [7], whose aim is to develop an efficient algorithm able to run within a mobile phone, that can provide useful feedback in the process of acquiring a diagnostically useful 12-lead Electrocardiography (ECG) recording. The method used to solve this problem is to apply a very powerful natural computing analysis tool, namely evolutionary neural networks, based on the joint evolution of the topology and the connection weights relying on a novel similarity-based crossover. The chapter focuses on discerning between usable and unusable electrocardiograms tele-medically acquired from mobile embedded devices. A preprocessing algorithm based on the Discrete Fourier Trasform has been applied before the evolutionary approach in order to extract the ECG feature dataset in the frequency domain. Finally, a series of tests has been carried out in order to evaluate the performance and the accuracy of the classifier system for such a challenge.

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“…For example, sensitivity analysis, fuzzy curves, MSE change, weight elimination and node pruning, and optimal brain damage (OBD) methods are measures that rank input feature importance. Some of these measures are heuristic (forward and backward selection), sensitivity index-based, are based on pseudo weights, rely on Garson's algorithm and some of its modified and extended versions that appear in the literature [11]. More specifically, in this work the concepts described in the following methodologies have been adopted for software cost drivers: Garson [1] proposed a method for partitioning the ANN connection weights to determine the relative importance of each input variable in the network (for more details see Section 3.2).…”

Section: Related Workmentioning

confidence: 99%

On the Problem of Attribute Selection for Software Cost Estimation: Input Backward Elimination Using Artificial Neural Networks

Papatheocharous

Andreou

2010

IFIP Advances in Information and Communication Technology

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Abstract. Many parameters affect the cost evolution of software projects. In the area of software cost estimation and project management the main challenge is to understand and quantify the effect of these parameters, or 'cost drivers', on the effort expended to develop software systems. This paper aims at investigating the effect of cost attributes on software development effort using empirical databases of completed projects and building Artificial Neural Network (ANN) models to predict effort. Prediction performance of various ANN models with different combinations of inputs is assessed in an attempt to reduce the models' input dimensions. The latter is performed by using one of the most popular saliency measures of network weights, namely Garson's Algorithm. The proposed methodology provides an insight on the interpretation of ANN which may be used for capturing nonlinear interactions between variables in complex software engineering environments.

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Neural networks for classification: a survey

Cited by 1,432 publications

References 173 publications

A dataset of human decision-making in teamwork management

A dataset of human decision-making in teamwork management

A Neuro-Evolutionary Approach to Electrocardiographic Signal Classification

On the Problem of Attribute Selection for Software Cost Estimation: Input Backward Elimination Using Artificial Neural Networks

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