Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes some of the most important developments in neural network classification research. Specifically, the issues of posterior probability estimation, the link between neural and conventional classifiers, learning and generalization tradeoff in classification, the feature variable selection, as well as the effect of misclassification costs are examined. Our purpose is to provide a synthesis of the published research in this area and stimulate further research interests and efforts in the identified topics.

/pdf/neural-networks-for-classification-a-survey-573tgz3hdm.pdf

Neural networks for classification: a survey

Objective: Most current Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately 10 years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs. Approach: We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons. Main results: We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods. Significance: This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these Review of Classification Algorithms for EEG-based BCI 2 methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI.

https://iopscience.iop.org/article/10.1088/1741-2552/aab2f2/pdf

A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update

8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.

Subset Selection in Regression

http://www.disp.uniroma2.it/earth_observation/pdf/vhr-urban-land-use.pdf

A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification

Architecture selection is a very important aspect in the design of neural networks (NNs) to optimally tune performance and computational complexity. Sensitivity analysis has been used successfully to prune irrelevant parameters from feedforward NNs. This paper presents a new pruning algorithm that uses the sensitivity analysis to quantify the relevance of input and hidden units. A new statistical pruning heuristic is proposed, based on the variance analysis, to decide which units to prune. The basic idea is that a parameter with a variance in sensitivity not significantly different from zero, is irrelevant and can be removed. Experimental results show that the new pruning algorithm correctly prunes irrelevant input and hidden units. The new pruning algorithm is also compared with standard pruning algorithms.

A new pruning heuristic based on variance analysis of sensitivity information

Variable selection with neural networks

Neural Networks -NN- have been used in a large variety of real-world applications. In those, one could measure a potentially large number N of variables Xi; probably not all Xi are equally informative: if one could select n« N “best” variables Xi, then one could reduce the amount of data to gather and process; hence reduce costs. Variable selection is thus an important issue in Pattern Recognition and Regression. It is also a complex problem; one needs a criterion to measure the value of a subset of variables and that value will of course depend on the predictor or classifier further used. Conventional variable selection techniques are based upon statistical or heuristics tools [Fukunaga, 90]: the major difficulty comes from the intrinsic combinatorics of the problem. In this paper we show how to use NNs for variable selection with a criterion based upon the evaluation of a variable usefulness. Various methods have been proposed to assess the value of a weight (e.g. saliency [Le Cun et al. 90] in the Optimal Brain-Damage -OBD- procedure): along similar ideas, we derive a method, called Optimal Cell Damage -OCD-, which evaluates the usefulness of input variables in a Multi-Layer Network and prunes the least useful. Variable selection is thus achieved during training of the classifier, ensuring that the selected set of variables matches the classifier complexity. Variable selection is thus viewed here as an extension of weight pruning. One can also use a regularization approach to variable selection, which we will discuss elsewhere [Cibas et al., 94]. We illustrate our method on two relatively small problems: prediction of a synthetic time series and classification of waveforms [Breiman et al., 84], representative of relatively hard problems.

Variable Selection with Optimal Cell Damage

Adaptative training of the non-linear single-layer perceptron can lead to the Euclidean distance classifier and later to the standard Fisher linear discriminant function. On the way between these two classifiers one has a regularized discriminant analysis. That is equivalent to the “weight decay” regularization term added to the cost function. Thus early stopping plays a role of regularization of the network.

Regularization by Early Stopping in Single Layer Perceptron Training

This paper presents simple and practical approaches for controlling the complexity of neural networks (NN) in order to optimize their generalization ability. Several formal and heuristic methods have been proposed in the literature for improving the performances of NNs. It is of major importance for the user to understand which cf these methods are of practical use and which are the more efficient. We will try here to fill the gap between specialists of these techniques and the user by presenting and analyzing some methods which we have selected both for their simplicity and efficiency. We will consider only supervised learning.

Complexity Control and Generalization in Multilayer Perceptrons

Practical complexity control in multilayer perceptrons

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