Innate immune memory and its application to artificial immune systems

The Journal of Supercomputing - The study of innate immune-based algorithms is an important research domain in Artificial Immune System (AIS), such as Dendritic Cell Algorithm (DCA), Toll-Like...     

LCMine: An efficient algorithm for mining discriminative regularities and its application in supervised classification

In this paper, we introduce an efficient algorithm for mining discriminative regularities on databases with mixed and incomplete data. Unlike previous methods, our algorithm does not apply an a priori discretization on numerical features; it extracts regularities from a set of diverse decision trees, induced with a special procedure. Experimental results show that a classifier based on the regularities obtained by our algorithm attains higher classification accuracy, using fewer discriminative regularities than those obtained by previous pattern-based classifiers. Additionally, we show that our classifier is competitive with traditional and state-of-the-art classifiers.     

A supervised discriminant data representation: application to pattern classification

Neural Computing and Applications - The performance of machine learning and pattern recognition algorithms generally depends on data representation. That is why, much of the current effort in...     

A hybrid artificial immune systems for multimodal function optimization and its application in engineering problem

Lately, the field of Artificial Immune Systems (AIS) has attracted wide attention among researchers as the algorithm is able to improve local searching ability and efficiency. However, the rate of convergence for AIS is rather slow as compared to other Evolutionary Algorithms. Alternatively, Particle Swarm Optimization (PSO) has been used effectively in solving complicated optimization problems with simple coding and lesser parameters, but it tends to converge prematurely. Thus, the good features of AIS and PSO are combined in order to reduce their shortcomings. By comparing the optimization results of the mathematical functions and the engineering problem using hybrid AIS (HAIS) and AIS, it is observed that HAIS has better performances in terms of accuracy, convergence rate and stability.     

A supervised clustering and classification algorithm for mining data with mixed variables

This paper presents a data mining algorithm based on supervised clustering to learn data patterns and use these patterns for data classification. This algorithm enables a scalable incremental learning of patterns from data with both numeric and nominal variables. Two different methods of combining numeric and nominal variables in calculating the distance between clusters are investigated. In one method, separate distance measures are calculated for numeric and nominal variables, respectively, and are then combined into an overall distance measure. In another method, nominal variables are converted into numeric variables, and then a distance measure is calculated using all variables. We analyze the computational complexity, and thus, the scalability, of the algorithm, and test its performance on a number of data sets from various application domains. The prediction accuracy and reliability of the algorithm are analyzed, tested, and compared with those of several other data mining algorithms.     

A fast neural net training algorithm and its application to speech classification

This paper describes a fast training algorithm for feedforward neural nets, as applied to a two-layer neural network to classify segments of speech as voiced, unvoiced, or silence. The speech classification method is based on five features computed for each speech segment and used as input to the network. The network weights are trained using a new fast training algorithm which minimizes the total least squares error between the actual output of the network and the corresponding desired output. The iterative training algorithm uses a quasi-Newtonian error-minimization method and employs a positive-definite approximation of the Hessian matrix to quickly converge to a locally optimal set of weights. Convergence is fast, with a local minimum typically reached within ten iterations; in terms of convergence speed, the algorithm compares favorably with other training techniques. When used for voiced-unvoiced-silence classification of speech frames, the network performance compares favorably with current approaches. Moreover, the approach used has the advantage of requiring no assumption of a particular probability distribution for the input features.     

A new ensemble feature selection and its application to pattern classification

Neural network ensemble based on rough sets reduct is proposed to decrease the computational complexity of conventional ensemble feature selection algorithm. First, a dynamic reduction technology combining genetic algorithm with resampling method is adopted to obtain reducts with good generalization ability. Second, Multiple BP neural networks based on different reducts are built as base classifiers. According to the idea of selective ensemble, the neural network ensemble with best generalization ability can be found by search strategies. Finally, classification based on neural network ensemble is implemented by combining the predictions of component networks with voting. The method has been verified in the experiment of remote sensing image and five UCI datasets classification. Compared with conventional ensemble feature selection algorithms, it costs less time and lower computing complexity, and the classification accuracy is satisfactory.     

A new parametric frequency-wavenumber spectrum estimation algorithm and its application to the analysis of 3-dimensional epicardial ECG signals

This paper presents a 3-dimensional (3-D) frequency-wavenumber spectrum estimation (FWSE) approach to the analysis of ECG signals. This approach treats the data as ‘wavefronts plus noise’ and provides a means of estimating key parameters associated with propagating wavefronts. A high resolution technique based on minimum variance representations of 3-D data fields (3-D CLS technique) is employed to obtain the FWSE. Computer simulation results that demonstrate the high resolution property of the technique when compared with the maximum-likelihood method of Capon are presented. Results of application of the technique to epicardial ECG data collected from a sensor array are also presented and discussed.     

A heuristic supervised Euclidean data difference dimension reduction for KNN classifier and its application to visual place classification

In this paper, we propose a novel supervised dimension reduction algorithm based on K-nearest neighbor (KNN) classifier. The proposed algorithm reduces the dimension of data in order to improve the accuracy of the KNN classification. This heuristic algorithm proposes independent dimensions which decrease Euclidean distance of a sample data and its K-nearest within-class neighbors and increase Euclidean distance of that sample and its M-nearest between-class neighbors. This algorithm is a linear dimension reduction algorithm which produces a mapping matrix for projecting data into low dimension. The dimension reduction step is followed by a KNN classifier. Therefore, it is applicable for high-dimensional multiclass classification. Experiments with artificial data such as Helix and Twin-peaks show ability of the algorithm for data visualization. This algorithm is compared with state-of-the-art algorithms in classification of eight different multiclass data sets from UCI collection. Simulation results have shown that the proposed algorithm outperforms the existing algorithms. Visual place classification is an important problem for intelligent mobile robots which not only deals with high-dimensional data but also has to solve a multiclass classification problem. A proper dimension reduction method is usually needed to decrease computation and memory complexity of algorithms in large environments. Therefore, our method is very well suited for this problem. We extract color histogram of omnidirectional camera images as primary features, reduce the features into a low-dimensional space and apply a KNN classifier. Results of experiments on five real data sets showed superiority of the proposed algorithm against others.     

A system to diagnose atherosclerosis via wavelet transforms,principal component analysis and artificial neural networks

In this study, Doppler ultrasound signals were acquired from carotid arteries of 82 patients with atherosclerosis and 95 healthy volunteers. We have employed discrete wave transform (DWT) of Doppler signals and power spectral density graphics of these decomposed signals using Welch method. After that, we have performed Principal component analysis (PCA) for data reduction and ANN in order to distinguish between atherosclerosis and healthy subjects.After the training phase, testing of the artificial neural network (ANN) was established. The overall results show that 97.9% correct classification was achieved, whereas two false classifications have been observed for the test group of 97 people.In conclusion we are proposing a complimentary expert system that can be coupled to software of the ultrasonic Doppler devices. The diagnosis performances of this study show the advantages of this system: it is rapid, easy to operate, noninvasive, inexpensive and making a decision without hesitation.     

Optimizing an artificial immune system algorithm in support of flow-Based internet traffic classification

The problem of classifying traffic flows in networks has become more and more important in recent times, and much research has been dedicated to it. In recent years, there has been a lot of interest in classifying traffic flows by application, based on the statistical features of each flow. Information about the applications that are being used on a network is very useful in network design, accounting, management, and security. In our previous work we proposed a classification algorithm for Internet traffic flow classification based on Artificial Immune Systems (AIS). We also applied the algorithm on an available data set, and found that the algorithm performed as well as other algorithms, and was insensitive to input parameters, which makes it valuable for embedded systems. It is also very simple to implement, and generalizes well from small training data sets. In this research, we expanded on the previous research by introducing several optimizations in the training and classification phases of the algorithm. We improved the design of the original algorithm in order to make it more predictable. We also give the asymptotic complexity of the optimized algorithm as well as draw a bound on the generalization error of the algorithm. Lastly, we also experimented with several different distance formulas to improve the classification performance. In this paper we have shown how the changes and optimizations applied to the original algorithm do not functionally change the original algorithm, while making its execution 50–60% faster. We also show that the classification accuracy of the Euclidian distance is superseded by the Manhattan distance for this application, giving 1–2% higher accuracy, making the accuracy of the algorithm comparable to that of a Naïve Bayes classifier in previous research that uses the same data set.     

Biomedical system based on the Discrete Hidden Markov Model using the Rocchio-Genetic approach for the classification of internal carotid artery Doppler signals

When the maximum likelihood approach (ML) is used during the calculation of the Discrete Hidden Markov Model (DHMM) parameters, DHMM parameters of the each class are only calculated using the training samples (positive training samples) of the same class. The training samples (negative training samples) not belonging to that class are not used in the calculation of DHMM model parameters. With the aim of supplying that deficiency, by involving the training samples of all classes in calculating processes, a Rocchio algorithm based approach is suggested. During the calculation period, in order to determine the most appropriate values of parameters for adjusting the relative effect of the positive and negative training samples, a Genetic algorithm is used as an optimization technique. The purposed method is used to classify the internal carotid artery Doppler signals recorded from 136 patients as well as of 55 healthy people. Our proposed method reached 97.38% classification accuracy with fivefold cross-validation (CV) technique. The classification results showed that the proposed method was effective for the classification of internal carotid artery Doppler signals.     

A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem

We propose a threshold-varying artificial neural network (TV-ANN) approach for solving the binary classification problem. Using a set of simulated and real-world data set for bankruptcy prediction, we illustrate that the proposed TV-ANN fares well, both for training and holdout samples, when compared to the traditional backpropagation artificial neural network (ANN) and the statistical linear discriminant analysis. The performance comparisons of TV-ANN with a genetic algorithm-based ANN and a classification tree approach C4.5 resulted in mixed results.     

一种新的免疫算法及其在多模态函数优化中的应用

提取免疫应答的部分简化机制并结合小生境技术,提出一种用于多峰值或非连续函数优化的免疫算法.该算法由记忆细胞获取、克隆选择、亲和突变及群体更新这四种算子模块构成.这些算子的有机组合不仅为最优化问题的解决提供了实用新方法,而且反映了抗体应答抗原的简化运行机制.算法设计的重点是借鉴小生境共享实现方法的思想建立有助于增强群体多样性及保留优良抗体的记忆细胞获取算子,以及利用亲和成熟机理设计抗体突变算子.所获算法具有整体和局部搜索能力及并行搜索特点.理论证明了其收敛性.仿真事例比较表明此算法不仅是有效的,而且能快速搜索到多个最优解(针对于多解最优化问题).     

A new optimization algorithm with application to nonlinear MPC

This paper investigates application of SQP optimization algorithms to nonlinear model predictive control. It considers feasible vs. infeasible path methods, sequential vs. simultaneous methods and reduced vs. full space methods. A new optimization algorithm coined rFOPT which remains feasibile with respect to inequality constraints is introduced. The suitable choices between these various strategies are assessed informally through a small CSTR case study. The case study also considers the effect various discretization methods have on the optimization problem.     

A modified object-oriented classification algorithm and its application in high-resolution remote-sensing imagery

High-resolution satellite images offer abundant information on the Earth's surface for remote-sensing applications. The traditional pixel-based image classification method only used by spectral information has been proved to have several drawbacks. To satisfactorily interpret high-resolution imagery, other important information such as geometry, texture and semantics must be used, which are represented not only in single pixels but in meaningful image objects. So, a modified high-resolution image classification algorithm with multi-characteristics based on objects is presented in this article. First, image objects are extracted by multi-scale multi-characteristic segmentation. Second, characteristics such as spectral information, geometry, texture and semantics are extracted by the corresponding extraction algorithm. Finally, the image objects are classified by means of fuzzy-logic classification with a weighted average calculation method. Preliminary results show promise in terms of classification quality and accuracy.     

A parallel genetic algorithm for adaptive hardware and its application to ECG signal classification

This paper presents a parallel genetic algorithm (GA) called the cellular compact genetic algorithm (c-cGA) and its implementation for adaptive hardware. An adaptive hardware based on the c-cGA is proposed to automate real-time classification of ECG signals. The c-cGA not only provides a strong search capability while maintaining genetic diversity using multiple GAs but also has a cellular-like structure and is a straight-forward algorithm suitable for hardware implementation. The c-cGA hardware and an adaptive digital filter structure also perform an adaptive feature selection in real time. The c-cGA is applied to a block-based neural network (BbNN) for online learning in the hardware. Using an adaptive hardware approach based on the c-cGA, an adaptive hardware system for classifying ECG signals is feasible. The proposed adaptive hardware can be implemented in a field programmable gate array (FPGA) for an adaptive embedded system applied to personalised ECG signal classifications for long-term patient monitoring.     

A new fusion formula and its application to continuous-time linear systems with multisensor environment

The problem of fusion of local estimates is considered. An optimal mean-square linear combination (fusion formula) of an arbitrary number of local vector estimates is derived. The derived result holds for all dynamic systems with measurements. In particular, for scalar uncorrelated local estimates, the fusion formula represents the well-known result in statistics. The fusion formula is applied to fusion of local Kalman estimates in multisensor filtering problem. Examples demonstrate high accuracy of the proposed fusion formula.     

A new measure of symmetry and its application to classification of bifurcating structures

This paper presents a new measure of symmetry for bifurcating structures, which relies not only on topology and ordering, but also on quantitative properties (e.g. length of branches). This measure is based on a specific biological mechanism and on the concept of minimum energy. The effectiveness of the approach is demonstrated in a classification test where leaves taken from plants growing under different stress conditions are classified. Results show that the proposed measure improves classification performance compared to classification based on other leading measures.     

引入人工偏好权重的混合型黑猩猩优化算法及应用

为提高黑猩猩优化算法的收敛速度、求解精度和局部极值逃逸能力,提出一种引入人工偏好权重的混合型黑猩猩优化算法(HChOA).首先,结合ChOA实际设计新的非线性收敛因子平衡算法全局和局部搜索能力;其次,在黑猩猩群体中引入“相异度”的概念和“趋异斥似”的人工偏好权重,以此优化黑猩猩位置更新公式,增强迭代末期种群多样性的同时加快算法收敛速度;最后,提出一种改进的算术优化算法(IAOA)并融入ChOA中,抽取部分黑猩猩个体执行IAOA优化策略,避免因领导者陷入局部最优而导致群体搜索停滞时出现早熟收敛现象.通过8个标准测试函数在多种维度下的数值对比实验以及1个工程设计问题的求解,综合分析验证了HChOA具有显著的优越性、稳定性和鲁棒性,且具备工程应用价值.