Nonlinear Class Boundaries Research Articles

An intrusion identification and prevention for cloud computing: From the perspective of deep learning

As the Internet industry has connected the globe using advancements in computer network from LAN to Cloud Infrastructure, Fashions and Developments in Internet of Things (IoT), Cloud Infrastructures, E-Commerce, Banking and Healthcare are allowing unimaginable access to devastating attacks.Intrusion detection is a critical component of information security, and the essential technique is the ability to properly recognise diverse network assaults. In this article, the methodology of an intrusion detection scheme utilising deep learning technique which is based on Fuzzy Min Max Neural Networks-Based Intrusion Detection System (FMMNN-IDS) has been proposed. The fuzzy min-max learning algorithm, an expansion-contraction method that can learn nonlinear class boundaries in a single pass through the data and gives the capacity to incorporate new and improve current classes without retraining, is used to identify the min-max points.Furthermore, the article investigated the model's performance in terms of binary and multiclass classification, as well as the count of learning rate and neurons affect the model's performance. The performance of FMMNN-IDS is compared with state-of-art approaches determined in the literatures. The FMMNN-IDS model increases accuracy rate of intrusion detection, achieves better intrusion detection rate and reduced error rate.

WCM based hybrid pre-processing algorithm for class imbalanced dataset

Imbalanced dataset classification is challenging because of the severely skewed class distribution. The traditional machine learning algorithms show degraded performance for these skewed datasets. However, there are additional characteristics of a classification dataset that are not only challenging for the traditional machine learning algorithms but also increase the difficulty when constructing a model for imbalanced datasets. Data complexity metrics identify these intrinsic characteristics, which cause substantial deterioration of the learning algorithms’ performance. Though many research efforts have been made to deal with class noise, none of them focused on imbalanced datasets coupled with other intrinsic factors. This paper presents a novel hybrid pre-processing algorithm focusing on treating the class-label noise in the imbalanced dataset, which suffers from other intrinsic factors such as class overlapping, non-linear class boundaries, small disjuncts, and borderline examples. This algorithm uses the wCM complexity metric (proposed for imbalanced dataset) to identify noisy, borderline, and other difficult instances of the dataset and then intelligently handles these instances. Experiments on synthetic datasets and real-world datasets with different levels of imbalance, noise, small disjuncts, class overlapping, and borderline examples are conducted to check the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm offers an interesting alternative to popular state-of-the-art pre-processing algorithms for effectively handling imbalanced datasets along with noise and other difficulties.

Multiple Feature Learning for Hyperspectral Image Classification

Hyperspectral image classification has been an active topic of research in recent years. In the past, many different types of features have been extracted (using both linear and nonlinear strategies) for classification problems. On the one hand, some approaches have exploited the original spectral information or other features linearly derived from such information in order to have classes which are linearly separable. On the other hand, other techniques have exploited features obtained through nonlinear transformations intended to reduce data dimensionality, to better model the inherent nonlinearity of the original data (e.g., kernels) or to adequately exploit the spatial information contained in the scene (e.g., using morphological analysis). Special attention has been given to techniques able to exploit a single kind of features, such as composite kernel learning or multiple kernel learning, developed in order to deal with multiple kernels. However, few approaches have been designed to integrate multiple types of features extracted from both linear and nonlinear transformations. In this paper, we develop a new framework for the classification of hyperspectral scenes that pursues the combination of multiple features. The ultimate goal of the proposed framework is to be able to cope with linear and nonlinear class boundaries present in the data, thus following the two main mixing models considered for hyperspectral data interpretation. An important characteristic of the presented approach is that it does not require any regularization parameters to control the weights of considered features so that different types of features can be efficiently exploited and integrated in a collaborative and flexible way. Our experimental results, conducted using a variety of input features and hyperspectral scenes, indicate that the proposed framework for multiple feature learning provides state-of-the-art classification results without significantly increasing computational complexity.

Differentiation of mangoes (Magnifera indica L.) conventional and organically cultivated according to their mineral content by using support vector machines

Mangoes of uniform genetics (Lippens variety) cultivated in the Gomera Island (Canary Islands) by conventional and organic farming were used to analyze the mineral content in order to differentiate crops cultivated in the same geographic area by the cultivation practices. Farming differences as well as soil differences may be reflected in the mineral content of the mangoes cultivated in these extensions. Concentration metal profiles consisting of the content of Ca, Co, Cu, Fe, K, Mg, Mn, Na, Ni and Zn in mangoes were obtained by using atomic absorption spectrometry (AAS). Pattern recognition classification procedures were applied for discriminating purposes. Linear discriminant analysis (LDA) allows to a classification performance of about 73% and support vector machines (SVM) found up to a 93% of prediction ability. The classification success when applying support vector machines techniques is due to their ability for modeling non-linear class boundaries.

Multiobjective GAs, Quantitative Indices, and Pattern Classification

The concept of multiobjective optimization (MOO) has been integrated with variable length chromosomes for the development of a nonparametric genetic classifier which can overcome the problems, like overfitting/overlearning and ignoring smaller classes, as faced by single objective classifiers. The classifier can efficiently approximate any kind of linear and/or nonlinear class boundaries of a data set using an appropriate number of hyperplanes. While designing the classifier the aim is to simultaneously minimize the number of misclassified training points and the number of hyperplanes, and to maximize the product of class wise recognition scores. The concepts of validation set (in addition to training and test sets) and validation functional are introduced in the multiobjective classifier for selecting a solution from a set of nondominated solutions provided by the MOO algorithm. This genetic classifier incorporates elitism and some domain specific constraints in the search process, and is called the CEMOGA-Classifier (constrained elitist multiobjective genetic algorithm based classifier). Two new quantitative indices, namely, the purity and minimal spacing, are developed for evaluating the performance of different MOO techniques. These are used, along with classification accuracy, required number of hyperplanes and the computation time, to compare the CEMOGA-Classifier with other related ones.

Multilayer statistical classifiers

A number of methods based on nonparametric regression have been developed in the last few years which are capable of approximating highly nonlinear class boundaries in classification problems. Bose (Comput. Statist. Data Anal. 22 (1996) 505) used additive splines for estimating the conditional class probabilities, and showed that the resulting method classification using splines (CUS) can achieve reasonably low misclassification error rates in many problems. This paper presents a powerful modification of CUS which we call the method of successive projections. This method can be used for any nonparametric regression based classification method but has been illustrated in this paper using mainly CUS, for simplicity and computational considerations. It seems to reduce the misclassification error rate of CUS in complex problems.

Theoretical performance of genetic pattern classifier

An investigation is carried out to formulate some theoretical results regarding the behavior of a genetic-algorithm-based pattern classification methodology, for an infinitely large number of training data points n, in an N-dimensional space R N . It is proved that for n→∞, and for a sufficiently large number of iterations, the performance of this classifier (when hyperplanes are considered to generate class boundaries) approaches that of the Bayes classifier, which is the optimal classifier when the class distributions and the a priori probabilities are known. It is shown that the optimum number of hyperplanes generated by the proposed classifier is equal to that required to model the Bayes decision boundary when there exists only one partition of the feature space that provides the Bayes error probability. Extensive experimental results on overlapping data sets following triangular and normal distributions with both linear and non-linear class boundaries are provided that conform to these claims. The claims also hold good when circular surfaces are considered as constituting elements/segments of boundaries. It is also shown experimentally that the variation of recognition score with a priori class probability for both the classifiers is similar.

Classification using splines

In a J class classification problem with data of the form: ( y n , x n ), n = 1,…, N, where y n ϵ{1,…, J} and x n = ( x 1 n ,…, x Mn ), linear discriminant analysis produces estimated class boundaries which are linear in x 1,…, x M . In this paper, a method is developed which estimates the conditional class probabilities in a function space which is bigger than the linear function space. The decision rule based on those estimated conditional class probabilities can have very nonlinear class boundaries. The method projects the conditional class probabilities onto a space spanned by cubic splines, and, hence, is called classification using splines (CUS). This new method seems to achieve comparable and in some cases lower misclassification error rates than existing methods like CART or the back-propagation neural network classifier.

Pattern classification with genetic algorithms

A method is proposed for finding decision boundaries, approximated by piecewise linear segments, for the classification of patterns in R 2 using an elitist model of a genetic algorithm. It involves the generation and placement of a set of lines (represented by strings) in the feature space that yields minimum misclassification. The effectiveness of the algorithm is demonstrated, for different parameter values, on both artificial data and speech data having non-linear class boundaries. Its comparison with the k-NN classifier is also made.

Fuzzy min-max neural networks. I. Classification

A supervised learning neural network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregate (union) of fuzzy set hyperboxes. A fuzzy set hyperbox is an n-dimensional box defined by a min point and a max point with a corresponding membership function. The min-max points are determined using the fuzzy min-max learning algorithm, an expansion-contraction process that can learn nonlinear class boundaries in a single pass through the data and provides the ability to incorporate new and refine existing classes without retraining. The use of a fuzzy set approach to pattern classification inherently provides a degree of membership information that is extremely useful in higher-level decision making. The relationship between fuzzy sets and pattern classification is described. The fuzzy min-max classifier neural network implementation is explained, the learning and recall algorithms are outlined, and several examples of operation demonstrate the strong qualities of this new neural network classifier.