Applying artificial neural network models to clinical decision making

Because psychological assessment typically lacks biological gold standards, it traditionally has relied on clinicians' expert knowledge. A more empirically based approach frequently has applied linear models to data to derive meaningful constructs and appropriate measures. Statistical inferences are then used to assess the generality of the findings. This article introduces artificial neural networks (ANNs), flexible nonlinear modeling techniques that test a model's generality by applying its estimates against "future" data. ANNs have potential for overcoming some shortcomings of linear models. The basics of ANNs and their applications to psychological assessment are reviewed. Two examples of clinical decision making are described in which an ANN is compared with linear models, and the complexity of the network performance is examined. Issues salient to psychological assessment are addressed.     

Is it worth generating rules from neural network ensembles?

Although many authors generated comprehensible models from individual networks, much less work has been done in the explanation of ensembles. DIMLP is a special neural network model from which rules are generated at the level of a single network and also at the level of an ensemble of networks. We applied ensembles of 25 DIMLP networks to several datasets of the public domain and a classification problem related to post-translational modifications of proteins. For the classification problems of the public domain, the average predictive accuracy of rulesets extracted from ensembles of neural networks was significantly better than the average predictive accuracy of rulesets generated from ensembles of decision trees. By varying the architectures of DIMLP networks we found that the average predictive accuracy of rules, as well as their complexity were quite stable. The comparison to other rule extraction techniques applied to neural networks showed that rules generated from DIMLP ensembles gave very good results. In the last problem related to bioinformatics, the best result obtained by ensembles of DIMLP networks was also significantly better than the best result obtained by ensembles of decision trees. Thus, although neural networks take much longer to train than decision trees and also rules are generated at a greater computational cost (however, still polynomial), at least for several classification problems it was worth using neural network ensembles, as extracted rules were more accurate, on average. The DIMLP software is available for PC-Linux under http://us.expasy.org/people/Guido.Bologna.html.     

How Do Artificial Neural Networks Classify Musical Triads? A Case Study in Eluding Bonini's Paradox

How might artificial neural networks (ANNs) inform cognitive science? Often cognitive scientists use ANNs but do not examine their internal structures. In this paper, we use ANNs to explore how cognition might represent musical properties. We train ANNs to classify musical chords, and we interpret network structure to determine what representations ANNs discover and use. We find connection weights between input units and hidden units can be described using Fourier phase spaces, a representation studied in musical set theory. We find the total signal coming through these weighted connection weights is a measure of the similarity between two Fourier structures: the structure of the hidden unit's weights and the structure of the stimulus. This is surprising because neither of these Fourier structures is computed by the hidden unit. We then show how output units use such similarity measures to classify chords. However, we also find different types of units—units that use different activation functions—use this similarity measure very differently. This result, combined with other findings, indicates that while our networks are related to the Fourier analysis of musical sets, they do not perform Fourier analyses of the kind usually described in musical set theory. Our results show Fourier representations of music are not limited to musical set theory. Our results also suggest how cognitive psychologists might explore Fourier representations in musical cognition. Critically, such theoretical and empirical implications require researchers to understand how network structure converts stimuli into responses.     

Rule extraction using ensemble of neural network ensembles

Neural networks are well-known for their impressive classification performance, and the ensemble learning technique acts as a catalyst to improve this performance even further by integrating multiple networks.However, neural network ensembles, like neural networks, are regarded as a black box because they cannot explain their decision-making process. As a result, despite their high classification performance, neural networks and their ensembles are unsuitable for some applications that require explainable decisions. However, the rule extraction technique can overcome this drawback by representing the knowledge learned by a neural network in the guise of interpretable decision rules. A rule extraction algorithm provides neural networks the ability to justify their classification responses using explainable classification rules. There are several rule extraction algorithms for extracting classification rules from neural networks, but only a few of them use neural network ensembles to generate rules. As a result, this paper proposes a rule extraction algorithm called Rule Extraction Using Ensemble of Neural Network Ensembles (RE-E-NNES) to demonstrate the high performance of neural network ensembles.RE-E-NNES extracts classification rules by ensembling several neural network ensembles. The results demonstrate the efficacy of the proposed RE-E-NNES algorithm in comparison to other existing rule extraction algorithms.     

人工神经网络在临床应用中的伦理思考

现代临床医学中的海量信息具有非线性特质,人工神经网络的自我学习、记忆和归纳功能,刚好适应了医学的新发展,在医学领域有良好的应用前景。事实上,人工神经网络临床应用的理论研究成果丰富,但商业应用并不多见。本文探讨人工神经网络在临床应用中可能引发的社会伦理争议,对其临床适用范围进行初步的论述。     

基于贝叶斯网模型的多级计分诊断测验分类及比较研究

贝叶斯网模型提供了一种方便和直观的框架结构来表示变量间的关系,非常适合在诊断测验中对教育评估的内容进行建模。本研究将两种贝叶斯网分类模型与序列多级计分诊断模型S-GDINA进行综合比较。考察两种贝叶斯网分类模型与S-GDINA在Q矩阵正确界定和包含一定比例(25%、 30%)的错误时,两者对被试的分类性能;并将贝叶斯网分类模型应用到实证数据中,展示贝叶斯网分类模型在实证数据中的分类过程和分类性能。研究结果表明：当Q矩阵由专家正确界定时,朴素贝叶斯分类模型的分类效果与S-GDINA模型相差不大,同样可以达到很好的分类效果,树增广的朴素贝叶斯分类模型的分类性能也能达到良好。实证结果进一步表明,将贝叶斯网分类模型应用于教育测量领域中的诊断分类工具是有其优势和可行的,尤其是当测验数据对于所选用诊断模型的拟合较差、测验的Q矩阵中包含错误或测验数据中包含较多的噪音时。     

Neural network applications in consumer behavior

This article introduces the concepts and terminology of artificial neural networks. The approach is demonstrated on data that represent a domain of interest to the consumer psychologist. ANNs are mathematical models that are commonly used in business applications to model relationships between variables. A key strength of ANNs is their flexibility, i.e. their ability to easily accommodate linear and non-linear relationships without any a priori functional form specification. They can easily be used to study topics of interest to consumer psychologists such as persuasion, influence, segmentation etc and can offer distinct advantages over traditional statistical techniques such as ANOVA and regression. We demonstrate the application of ANNs in three different areas: regression, non-linear principal component analysis and classification.     

Training redundant artificial neural networks: Imposing biology on technology

One biological principle that is often overlooked in the design of artificial neural networks (ANNs) is redundancy. Redundancy is the replication of processes within the brain. This paper examines the effects of redundancy on learning in ANNs when given either a function-approximation task or a pattern-classification task. The function-approximation task simulated a robotic arm reaching toward an object in two-dimensional space, and the pattern-classification task was detecting parity. Results indicated that redundant ANNs learned the pattern-classification problem much faster, and converge on a solution 100% of the time, whereas standard ANNs sometimes failed to learn the problem. Furthermore, when overall network error is considered, redundant ANNs were significantly more accurate than standard ANNs in performing the function-approximation task. These results are discussed in terms of the relevance of redundancy to the performance of ANNs in general, and the relevance of redundancy in biological systems in particular.     

Statistical inference: learning in artificial neural networks

Artificial neural networks (ANNs) are widely used to model low-level neural activities and high-level cognitive functions. In this article, we review the applications of statistical inference for learning in ANNs. Statistical inference provides an objective way to derive learning algorithms both for training and for evaluation of the performance of trained ANNs. Solutions to the over-fitting problem by model-selection methods, based on either conventional statistical approaches or on a Bayesian approach, are discussed. The use of supervised and unsupervised learning algorithms for ANNs are reviewed. Training a multilayer ANN by supervised learning is equivalent to nonlinear regression. The ensemble methods, bagging and arching, described here, can be applied to combine ANNs to form a new predictor with improved performance. Unsupervised learning algorithms that are derived either by the Hebbian law for bottom-up self-organization, or by global objective functions for top-down self-organization are also discussed.     

Artificial neural networks in Neurosciences

This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.     

Cognitive computing models for estimation of reference evapotranspiration: A review

Irrigation practices can be advanced by the aid of cognitive computing models. Repeated droughts, population expansion and the impact of global warming collectively impose rigorous restrictions over irrigation practices. Reference evapotranspiration (ET₀) is a vital factor to predict the crop water requirements based on climate data. There are many techniques available for the prediction of ET₀. An efficient ET₀ prediction model plays an important role in irrigation system to increase water productivity. In the present study, a review has been carried out over cognitive computing models used for the estimation of ET₀. Review exhibits that artificial neural network (ANN) approach outperforms support vector machine (SVM) and genetic programming (GP). Second order neural network (SONN) is the most promising approach among ANN models.     

Hierarchical growing grid networks for skeleton based action recognition

In this paper, a novel cognitive architecture for action recognition is developed by applying layers of growing grid neural networks. Using these layers makes the system capable of automatically arranging its representational structure. In addition to the expansion of the neural map during the growth phase, the system is provided with a prior knowledge of the input space, which increases the processing speed of the learning phase. Apart from two layers of growing grid networks the architecture is composed of a preprocessing layer, an ordered vector representation layer and a one-layer supervised neural network. These layers are designed to solve the action recognition problem. The first-layer growing grid receives the input data of human actions and the neural map generates an action pattern vector representing each action sequence by connecting the elicited activation of the trained map. The pattern vectors are then sent to the ordered vector representation layer to build the time-invariant input vectors of key activations for the second-layer growing grid. The second-layer growing grid categorizes the input vectors to the corresponding action clusters/sub-clusters and finally the one-layer supervised neural network labels the shaped clusters with action labels. Three experiments using different datasets of actions show that the system is capable of learning to categorize the actions quickly and efficiently. The performance of the growing grid architecture is compared with the results from a system based on Self-Organizing Maps, showing that the growing grid architecture performs significantly superior on the action recognition tasks.     

Human age classification using facial skin aging features and artificial neural network

In this paper a novel method based on facial skin aging features and Artificial Neural Network (ANN) is proposed to classify the human face images into four age groups. The facial skin aging features are extracted by using Local Gabor Binary Pattern Histogram (LGBPH) and wrinkle analysis. The ANN classifier is designed by using two layer feedforward backpropagation neural networks. The proposed age classification framework is trained and tested with face images from PAL face database and shown considerable improvement in the age classification accuracy up to 94.17% and 93.75% for male and female respectively.     

Support vector machine for classification of walking conditions of persons after stroke with dropped foot

Walking with dropped foot represents a major gait disorder, which is observed in hemiparetic persons after stroke. This study explores the use of support vector machine (SVMs) to classify different walking conditions for hemiparetic subjects. Seven participants with dropped foot (category 4 of functional ambulatory category) walked in five different conditions: level ground, stair ascent, stair descent, upslope, and downslope. The kinematic data were measured by two portable sensor units, each comprising an accelerometer and gyroscope attached to the lower limb on the shank and foot segments. The overall classification accuracy of stair ascent, stair descent, and other walking conditions was 92.9% using input features from the sensor attached to the shank. It was further improved to 97.5% by adding two more inputs from the sensor attached to the foot. Stair ascent was also classified by the inputs from the foot sensor unit with 96% accuracy. The performance of an SVM was shown to be superior to that of other machine learning methods using artificial neural networks (ANN) and radial basis function neural networks (RBF). The results suggested that the SVM classification method could be applied as a tool for pathological gait analysis, pattern recognition, control signals in functional electrical stimulation (FES) and rehabilitation robot, as well as activity monitoring during rehabilitation of daily activities.     

A study of the classification capabilities of neural networks using unsupervised learning: A comparison withK-means clustering

Several neural networks have been proposed in the general literature for pattern recognition and clustering, but little empirical comparison with traditional methods has been done. The results reported here compare neural networks using Kohonen learning with a traditional clustering method (K-means) in an experimental design using simulated data with known cluster solutions. Two types of neural networks were examined, both of which used unsupervised learning to perform the clustering. One used Kohonen learning with a conscience and the other used Kohonen learning without a conscience mechanism. The performance of these nets was examined with respect to changes in the number of attributes, the number of clusters, and the amount of error in the data. Generally, theK-means procedure had fewer points misclassified while the classification accuracy of neural networks worsened as the number of clusters in the data increased from two to five.Acknowledgements: Sara Dickson, Vidya Nair, and Beth Means assisted with the neural network analyses.     

An aphasia database on the internet: a model for computer-assisted analysis in aphasiology

A web-based software model was developed as an example for data mining in aphasiology. It is used for educating medical and engineering students. It is based upon a database of 254 aphasic patients which contains the diagnosis of the aphasia type, profiles of an aphasia test battery (Aachen Aphasia Test), and some further clinical information. In addition, the cerebral lesion profiles of 147 of these cases were standardized by transferring the coordinates of the lesions to a 3D reference brain based upon the ACPC coordinate system. Two artificial neural networks were used to perform a classification of the aphasia type. First, a coarse classification was achieved by using an assessment of spontaneous speech of the patient which produced correct results in 87% of the test cases. Data analysis tools were used to select four features of the 30 available test features to yield a more accurate diagnosis. This classifier produced correct results in 92% of the test cases. The neural network approach is similar to grouping performed in group studies, while the nearest-neighbor method shows a design more similar to case studies. It finds the neurolinguistic and the lesion data of patients whose AAT profiles are most similar to the user's input. This way lesion profiles can be compared to each other interindividually. The Aphasia Diagnoser is available on the Web address http://fuzzy.iau.dtu.dk/aphasia.nsf and thus should facilitate a discussion about the reliability and possibilities of data-mining techniques in aphasiology.     

粗糙集和神经网络在心理测量中的应用

探讨当因素分析和多元回归方法的使用条件未得到满足时,是否可采用粗糙集方法进行观察变量的精简,以及是否可采用神经网络方法进行预测效度检验。理论分析了粗糙集和神经网络在心理测量中应用的可能性,并运用粗糙集对于人事干部胜任力评估数据进行分析,比较了7种离散化方法和2种约简算法构成的14种组合,发现当采用Manual方法进行离散化、遗传算法进行约简时,能够很好地对观测变量进行精简;运用概率神经网络能够比等级回归方法更好地进行预测效度检验。研究结果表明对于处理心理测量中的非等距变量,粗糙集和神经网络是非常有用的方法     

Applications of neural networks in training science

Training science views itself as an integrated and applied science, developing practical measures founded on scientific method. Therefore, it demands consideration of a wide spectrum of approaches and methods. Especially in the field of competitive sports, research questions are usually located in complex environments, so that mainly field studies are drawn upon to obtain broad external validity. Here, the interrelations between different variables or variable sets are mostly of a nonlinear character. In these cases, methods like neural networks, e.g., the pattern recognizing methods of Self-Organizing Kohonen Feature Maps or similar instruments to identify interactions might be successfully applied to analyze data. Following on from a classification of data analysis methods in training-science research, the aim of the contribution is to give examples of varied sports in which network approaches can be effectually used in training science. First, two examples are given in which neural networks are employed for pattern recognition. While one investigation deals with the detection of sporting talent in swimming, the other is located in game sports research, identifying tactical patterns in team handball. The third and last example shows how an artificial neural network can be used to predict competitive performance in swimming.     

基于人工神经网络的一种效度凭证求取方法

针对基于统计学的效度凭证求取方法所存在的问题,提出了基于人工神经网络的效度凭证求取方法。蒙特卡罗模拟实验和对实际数据的分析表明,当测验分数和效标分数为单变量且非线性关系时,或者测验分数和效标分数为多变量时,运用神经网络方法可以比统计学方法更好地求取心理测验效度的基于和其他变量关系的凭证。     

Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals

ABSTRACT— Brain-imaging research has largely focused on localizing patterns of activity related to specific mental processes, but recent work has shown that mental states can be identified from neuroimaging data using statistical classifiers. We investigated whether this approach could be extended to predict the mental state of an individual using a statistical classifier trained on other individuals, and whether the information gained in doing so could provide new insights into how mental processes are organized in the brain. Using a variety of classifier techniques, we achieved cross-validated classification accuracy of 80% across individuals (chance = 13%). Using a neural network classifier, we recovered a low-dimensional representation common to all the cognitive-perceptual tasks in our data set, and we used an ontology of cognitive processes to determine the cognitive concepts most related to each dimension. These results revealed a small organized set of large-scale networks that map cognitive processes across a highly diverse set of mental tasks, suggesting a novel way to characterize the neural basis of cognition.