Selective negative correlation learning approach to incremental learning

Negative correlation learning (NCL) is a successful approach to constructing neural network ensembles. In batch learning mode, NCL outperforms many other ensemble learning approaches. Recently, NCL has also shown to be a potentially powerful approach to incremental learning, while the advantages of NCL have not yet been fully exploited. In this paper, we propose a selective NCL (SNCL) algorithm for incremental learning. Concretely, every time a new training data set is presented, the previously trained neural network ensemble is cloned. Then the cloned ensemble is trained on the new data set. After that, the new ensemble is combined with the previous ensemble and a selection process is applied to prune the whole ensemble to a fixed size. This paper is an extended version of our preliminary paper on SNCL. Compared to the previous work, this paper presents a deeper investigation into SNCL, considering different objective functions for the selection process and comparing SNCL to other NCL-based incremental learning algorithms on two more real world bioinformatics data sets. Experimental results demonstrate the advantage of SNCL. Further, comparisons between SNCL and other existing incremental learning algorithms, such Learn++ and ARTMAP, are also presented.     

基于标签正负相关性的多标签类属特征学习

提出一种基于标签正负相关性的多标签类属特征学习方法(multi-label learning with label-specific features based on positive and negative label correlation,LIFTPNL).基于k近邻的思想构建全局和局部的标签信息矩阵,根据此...     

Automatic identification of music performers with learning ensembles

This article addresses the problem of identifying the most likely music performer, given a set of performances of the same piece by a number of skilled candidate pianists. We propose a set of very simple features for representing stylistic characteristics of a music performer, introducing ‘norm-based’ features that relate to a kind of ‘average’ performance. A database of piano performances of 22 pianists playing two pieces by Frédéric Chopin is used in the presented experiments. Due to the limitations of the training set size and the characteristics of the input features we propose an ensemble of simple classifiers derived by both subsampling the training set and subsampling the input features. Experiments show that the proposed features are able to quantify the differences between music performers. The proposed ensemble can efficiently cope with multi-class music performer recognition under inter-piece conditions, a difficult musical task, displaying a level of accuracy unlikely to be matched by human listeners (under similar conditions).     

基于负相关性增强的不平衡多标签学习算法

由于标签空间过大,标签分布不平衡问题在多标签数据集中广泛存在,解决该问题在一定程度上可以提高多标签学习的分类性能.通过标签相关性提升分类性能是解决该问题的一种最常见的有效策略,众多学者进行了大量研究,然而这些研究更多地是采用基于正相关性策略提升性能.在实际问题中,除了正相关性外,标签的负相关性也可能存在,如果在考虑正相...     

Combining features of negative correlation learning with mixture of experts in proposed ensemble methods

Both theoretical and experimental studies have shown that combining accurate neural networks (NNs) in the ensemble with negative error correlation greatly improves their generalization abilities. Negative correlation learning (NCL) and mixture of experts (ME), two popular combining methods, each employ different special error functions for the simultaneous training of NNs to produce negatively correlated NNs. In this paper, we review the properties of the NCL and ME methods, discussing their advantages and disadvantages. Characterization of both methods showed that they have different but complementary features, so if a hybrid system can be designed to include features of both NCL and ME, it may be better than each of its basis approaches. In this study, two approaches are proposed to combine the features of both methods in order to solve the weaknesses of one method with the strength of the other method, i.e., gated-NCL (G-NCL) and mixture of negatively correlated experts (MNCE). In the first approach, G-NCL, a dynamic combiner of ME is used to combine the outputs of base experts in the NCL method. The suggested combiner method provides an efficient tool to evaluate and combine the NCL experts by the weights estimated dynamically from the inputs based on the different competences of each expert regarding different parts of the problem. In the second approach, MNCE, the capability of a control parameter for NCL is incorporated in the error function of ME, which enables the training algorithm of ME to efficiently adjust the measure of negative correlation between the experts. This control parameter can be regarded as a regularization term added to the error function of ME to establish better balance in bias–variance–covariance trade-offs and thus improves the generalization ability. The two proposed hybrid ensemble methods, G-NCL and MNCE, are compared with their constituent methods, ME and NCL, in solving several benchmark problems. The experimental results show that our proposed methods preserve the advantages and alleviate the disadvantages of their basis approaches, offering significantly improved performance over the original methods.     

Threaded ensembles of autoencoders for stream learning

Anomaly detection in streaming data is an important problem in numerous application domains. Most existing model‐based approaches to stream learning are based on decision trees due to their fast construction speed. This paper introduces streaming autoencoder (SA), a fast and novel anomaly detection algorithm based on ensembles of neural networks for evolving data streams. It is a one‐class learner, which only requires data from the positive class for training and is accurate even when anomalous training data are rare. It features an ensemble of threaded autoencoders with continuous learning capacity. Furthermore, the SA uses a 2‐step detection mechanism to ensure that real anomalies are detected with low false‐positive rates. The method is highly efficient because it processes data streams in parallel with multithreads and alternating buffers. Our analysis shows that SA has a linear runtime and requires constant memory space. Empirical comparisons to the state‐of‐the‐art methods on multiple benchmark data sets demonstrate that the proposed method detects anomalies efficiently with fewer false alarms.     

Evolutionary extreme learning machine

Extreme learning machine (ELM) [G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN2004), Budapest, Hungary, 25-29 July 2004], a novel learning algorithm much faster than the traditional gradient-based learning algorithms, was proposed recently for single-hidden-layer feedforward neural networks (SLFNs). However, ELM may need higher number of hidden neurons due to the random determination of the input weights and hidden biases. In this paper, a hybrid learning algorithm is proposed which uses the differential evolutionary algorithm to select the input weights and Moore-Penrose (MP) generalized inverse to analytically determine the output weights. Experimental results show that this approach is able to achieve good generalization performance with much more compact networks.     

Discovering predictive ensembles for transfer learning and meta-learning

Recent meta-learning approaches are oriented towards algorithm selection, optimization or recommendation of existing algorithms. In this article we show how data-tailored algorithms can be constructed from building blocks on small data sub-samples. Building blocks, typically weak learners, are optimized and evolved into data-tailored hierarchical ensembles. Good-performing algorithms discovered by evolutionary algorithm can be reused on data sets of comparable complexity. Furthermore, these algorithms can be scaled up to model large data sets. We demonstrate how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art approaches on the Airline data set. Evolved hierarchical ensembles can therefore be beneficial as algorithmic building blocks in meta-learning, including meta-learning at scale.     

Evolutionary learning of fuzzy models

This paper presents an evolutionary algorithm for generating knowledge bases for fuzzy logic systems. The algorithm dynamically adjusts the focus of the genetic search by dividing the population into three sub-groups, each concerned with a different level of knowledge base optimisation. The algorithm was tested on the identification of two highly non-linear simulated plants. Such a task represents a challenging test for any learning technique and involves two opposite requirements, the exploration of a large high-dimensional search space and the achievement of the best modelling accuracy. The algorithm achieved learning results that compared favourably with those for alternative knowledge base generation methods.     

Evolutionary learning of nearest-neighbor MLP

The nearest-neighbor multilayer perceptron (NN-MLP) is a single-hidden-layer network suitable for pattern recognition. To design an NN-MLP efficiently, this paper proposes a new evolutionary algorithm consisting of four basic operations: recognition, remembrance, reduction, and review. Experimental results show that this algorithm can produce the smallest or nearly smallest networks from random initial ones.     

Trial-and-error correlation learning

A new learning architecture is proposed for hardware implementation of neural networks. In this architecture, each synaptic weight is intentionally changed for each trial and then modified in proportion to the trial-and-error correlation between the changes in the weight and the total output error. If the weight changes are small, this learning is almost as good as the backpropagation (BP) learning, without requiring a complex backward network for error backpropagation. If the changes are large, the weights can move in the weight space without being restricted to a relatively small local-minimum. Computer simulation shows that this learning surpasses BP learning in converging to the global minimum when the trial-and-error correlation is defined so as to emphasize the gain (i.e., the decrease in the total output error) rather than the loss.     

Dynamically building diversified classifier pruning ensembles via canonical correlation analysis

Empirical studies on ensemble learning that combines multiple classifiers have shown that, it is an effective technique to improve accuracy and stability of a single classifier. In this paper, we propose a novel method of dynamically building diversified sparse ensembles. We first apply a technique known as the canonical correlation to model the relationship between the input data variables and output base classifiers. The canonical (projected) output classifiers and input training data variables are encoded globally through a multi-linear projection of CCA, to decrease the impacts of noisy input data and incorrect classifiers to a minimum degree in such a global view. Secondly, based on the projection, a sparse regression method is used to prune representative classifiers by combining classifier diversity measurement. Based on the above methods, we evaluate the proposed approach by several datasets, such as UCI and handwritten digit recognition. Experimental results of the study show that, the proposed approach achieves better accuracy as compared to other ensemble methods such as QFWEC, Simple Vote Rule, Random Forest, Drep and Adaboost.

     

Evolutionary learning of hierarchical decision rules

This paper describes an approach based on evolutionary algorithms, hierarchical decision rules (HIDER), for learning rules in continuous and discrete domains. The algorithm produces a hierarchical set of rules, that is, the rules are sequentially obtained and must therefore be tried until one is found whose conditions are satisfied. Thus, the number of rules may be reduced because the rules could be inside of one another. The evolutionary algorithm uses both real and binary coding for the individuals of the population. We tested our system on real data from the UCI repository, and the results of a ten-fold cross-validation are compared to C4.5s, C4.5Rules, See5s, and See5Rules. The experiments show that HIDER works well in practice.     

Evolutionary learning of dynamic probabilistic models with large time lags

In this paper, we explore the automatic explanation of multivariate time series (MTS) through learning dynamic Bayesian networks (DBNs). We have developed an evolutionary algorithm which exploits certain characteristics of MTS in order to generate good networks as quickly as possible. We compare this algorithm to other standard learning algorithms that have traditionally been used for static Bayesian networks but are adapted for DBNs in this paper. These are extensively tested on both synthetic and real-world MTS for various aspects of efficiency and accuracy. By proposing a simple representation scheme, an efficient learning methodology, and several useful heuristics, we have found that the proposed method is more efficient for learning DBNs from MTS with large time lags, especially in time-demanding situations. © 2001 John Wiley & Sons, Inc.     

基于演化深度学习的图像描述自动生成技术研究

针对现有基于视觉注意力和基于文本注意力的图像描述自动生成模型无法同时兼顾描述图像细节和整体图像的问题,提出了一种基于演化深度学习的图像描述生成模型(evolutionary deep learning model for image captioning, EDLMIC),该模型是一种包含图像编码器、演化神经网络和自适应融合解码器三个子模块的图像描述自动生成模型,能够有效地融合视觉信息和文本信息,自动计算这两种信息在每个时间步所占的比例,从而基于融合的视觉文本信息更好地生成给定图像的相关描述。在Flickr30K和COCO2014两个公开数据集的实验结果表明,EDLMIC模型在METEOR、ROUGE-L、CIDEr和SPICE四个指标均优于其他基线模型,并且在多种不同的生活场景中具有较好的性能。     

Unsupervised learning of arbitrarily shaped clusters using ensembles of Gaussian models

We propose a new clustering algorithm, called SyMP, which is based on synchronization of pulse-coupled oscillators. SyMP represents each data point by an Integrate-and-Fire oscillator and uses the relative similarity between the points to model the interaction between the oscillators. SyMP is robust to noise and outliers, determines the number of clusters in an unsupervised manner, and identifies clusters of arbitrary shapes. The robustness of SyMP is an intrinsic property of the synchronization mechanism. To determine the optimum number of clusters, SyMP uses a dynamic and cluster dependent resolution parameter. To identify clusters of various shapes, SyMP models each cluster by an ensemble of Gaussian components. SyMP does not require the specification of the number of components for each cluster. This number is automatically determined using a dynamic intra-cluster resolution parameter. Clusters with simple shapes would be modeled by few components while clusters with more complex shapes would require a larger number of components. The proposed clustering approach is empirically evaluated with several synthetic data sets, and its performance is compared with GK and CURE. To illustrate the performance of SyMP on real and high-dimensional data sets, we use it to categorize two image databases.     

On learning effective ensembles of deep neural networks for intrusion detection

Classification-oriented Machine Learning methods are a precious tool, in modern Intrusion Detection Systems (IDSs), for discriminating between suspected intrusion attacks and normal behaviors. Many recent proposals in this field leveraged Deep Neural Network (DNN) methods, capable of learning effective hierarchical data representations automatically. However, many of these solutions were validated on data featuring stationary distributions and/or large amounts of training examples. By contrast, in real IDS applications different kinds of attack tend to occur over time, and only a small fraction of the data instances is labeled (usually with far fewer examples of attacks than of normal behavior). A novel ensemble-based Deep Learning framework is proposed here that tries to face the challenging issues above. Basically, the non-stationary nature of IDS log data is faced by maintaining an ensemble consisting of a number of specialized base DNN classifiers, trained on disjoint chunks of the data instances’ stream, plus a combiner model (reasoning on both the base classifiers predictions and original instance features). In order to learn deep base classifiers effectively from small training samples, an ad-hoc shared DNN architecture is adopted, featuring a combination of dropout capabilities, skip-connections, along with a cost-sensitive loss (for dealing with unbalanced data). Tests results, conducted on two benchmark IDS datasets and involving several competitors, confirmed the effectiveness of our proposal (in terms of both classification accuracy and robustness to data scarcity), and allowed us to evaluate different ensemble combination schemes.     

Feature elimination based random subspace ensembles learning for ECG arrhythmia diagnosis

Various methods for ensembles selection and classifier combination have been designed to optimize the performance of ensembles of classifiers. However, use of large number of features in training data can affect the classification performance of machine learning algorithms. The objective of this paper is to represent a novel feature elimination (FE) based ensembles learning method which is an extension to an existing machine learning environment. Here the standard 12 lead ECG signal recordings data have been used in order to diagnose arrhythmia by classifying it into normal and abnormal subjects. The advantage of the proposed approach is that it reduces the size of feature space by way of using various feature elimination methods. The decisions obtained from these methods have been coalesced to form a fused data. Thus the idea behind this work is to discover a reduced feature space so that a classifier built using this tiny data set would perform no worse than a classifier built from the original data set. Random subspace based ensembles classifier is used with PART tree as base classifier. The proposed approach has been implemented and evaluated on the UCI ECG signal data. Here, the classification performance has been evaluated using measures such as mean absolute error, root mean squared error, relative absolute error, F-measure, classification accuracy, receiver operating characteristics and area under curve. In this way, the proposed novel approach has provided an attractive performance in terms of overall classification accuracy of 91.11 % on unseen test data set. From this work, it is shown that this approach performs well on the ensembles size of 15 and 20.     

LODE: A distance-based classifier built on ensembles of positive and negative observations

Current work on assembling a set of local patterns such as rules and class association rules into a global model for the prediction of a target usually focuses on the identification of the minimal set of patterns that cover the training data. In this paper we present a different point of view: the model of a class has been built with the purpose to emphasize the typical features of the examples of the class. Typical features are modeled by frequent itemsets extracted from the examples and constitute a new representation space of the examples of the class. Prediction of the target class of test examples occurs by computation of the distance between the vector representing the example in the space of the itemsets of each class and the vectors representing the classes.It is interesting to observe that in the distance computation the critical contribution to the discrimination between classes is given not only by the itemsets of the class model that match the example but also by itemsets that do not match the example. These absent features constitute some pieces of information on the examples that can be considered for the prediction and should not be disregarded. Second, absent features are more abundant in the wrong classes than in the correct ones and their number increases the distance between the example vector and the negative class vectors. Furthermore, since absent features are frequent features in their respective classes, they make the prediction more robust against over-fitting and noise. The usage of features absent in the test example is a novel issue in classification: existing learners usually tend to select the best local pattern that matches the example and do not consider the abundance of other patterns that do not match it. We demonstrate the validity of our observations and the effectiveness of LODE, our learner, by means of extensive empirical experiments in which we compare the prediction accuracy of LODE with a consistent set of classifiers of the state of the art. In this paper we also report the methodology that we adopted in order to determine automatically the setting of the learner and of its parameters.     

基于结构性质保持和相关性学习的多标记分类算法

现有的多标记学习技术大多只考虑了相关性学习问题而忽略了数据因变换而引起的结构性质不一致问题,导致原始特征数据的结构性质因映射变换发生改变,从而影响了模型的分类性能。为了解决这一问题,提出了基于结构性质保持和相关性学习的多标记分类算法。首先,构造了线性映射函数以实现特征空间与标记空间的映射;然后借鉴图正则化思想,引入基于特征数据的结构性质保持策略以降低特征数据因线性变换引起的结构性质差异;最后,针对标记数据引入基于标记对的相关性学习策略进一步优化算法参数,以提高模型的分类性能。在不同规模的标准数据集上进行测试,结果表明所提算法与一些流行的多标记分类算法相比具有更优的分类性能,验证了所提算法的有效性。