Boundary‐based Fuzzy‐SVDD for one‐class classification

Density weighted support vector data description

Support vector data description (SVDD) is a well-known kernel-based one-class classification method that exhibits intrinsic regularization ability and robustness versus low numbers of high-dimensional samples. However, the efficiency of SVDD is limited by the cubic time complexity. To solve this problem, this paper first investigates the effect of selecting a reduced subset as the training set of SVDD, while guaranteeing the classification quality. To this end, a new heuristic sample condensed rule, termed HSC, is proposed to accurately identify those potential support vectors that characterize the classification boundary. HSC can consider both the spatial distribution and local density features of training samples, and focus on selecting samples very close to the decision boundary. When dealing with the local density computation, we introduce the idea of K nearest neighbors (KNN) to examine the density of samples in the neighbors of the object to be classified. Finally, a condensed but informative subset obtained by HSC will be applied to train SVDD breezily. The experimental results show that HSC-based SVDD sensibly improves over conventional SVDD, in terms of the size of the training set while guaranteeing a comparable classification quality. In addition, it is competitive over other improved SVDD classifiers in terms of training and testing time.

In this paper, we perform diagnostic pattern recognition on a gene-expression profile data set by using one-class classification. Unlike conventional multiclass classifiers, the one-class (OC) classifier is built on one class only. For optimal performance, it accepts samples coming from the class used for training and rejects all samples from other classes. We evaluate six OC classifiers: the Gaussian model, Parzen windows, support vector data description (with two types of kernels: inner product and Gaussian), nearest neighbor data description, K-means, and PCA on three gene-expression profile data sets, those being an SRBCT data set, a Colon data set, and a Leukemia data set. Providing there is a good splitting of training and test samples and feature selection, most OC classifiers can produce high quality results. Parzen windows and support vector data description are "over-strict" in most cases, while nearest neighbor data description is "over-loose". Other classifiers are intermediate between these two extremes. The main difficulty for the OC classifier is it is difficult to obtain an optimum decision threshold if there are a limited number of training samples.

SVDD boundary and DPC clustering technique-based oversampling approach for handling imbalanced and overlapped data

Information entropy based sample reduction for support vector data description

When the ship is damaged after weapon attack, it is necessary for commanders to recognise its unsinkability grade quickly. Through unsinkability classification, we can know whether the ship will sink or not and its sinking probability. The unsinkability classification is a N-class pattern recognition problem. The fuzzy support vector machine (FSVM) is used to distinguish a certain unsinkability grade from other unsinkability grades firstly. Concerning the definition of fuzzy membership is critical in FSVM, the support vector data description (SVDD) is used to found fuzzy membership function. Through samples test, we found that FSVM of which fuzzy membership calculated through SVDD has better classification efficiency and precision.

With the development of deep convolutional neural networks in recent years, the network structure has become more and more complicated and varied, and there are very good results in pattern recognition, image classification, scene classification, and target tracking. This end-to-end learning model relies on the initial large dataset. However, many data are gradually obtained in practical situations, which contradict the deep learning of one-time batch learning. There is an urgent need for an incremental learning approach that can continuously learn new knowledge from new data while retaining what has already been learned. This paper proposes an incremental learning algorithm based on convolutional neural network and support vector data description. CNN and AM-Softmax loss function are used to represent and continuously learn image features. Support vector data description is used to construct multiple hyperspheres for new and old classes of images. Class-incremental learning is achieved by the increment of hyperspheres. The experimental results show that the incremental learning method proposed in this paper can effectively extract the latent features of the image and adapt it to the learning situation of the class-increment. The recognition accuracy is close to batch learning.

A comparative investigation of data-driven approaches based on one-class classifiers for condition monitoring of marine machinery system

Monitoring of solid-state fermentation of wheat straw in a pilot scale using FT-NIR spectroscopy and support vector data description

The paper aims to solve the problem of insufficient high risk data in risk assessment of R&D projects. A one-class classification method called support vector data description (SVDD) is studied, and an intelligent risk assessment model based on SVDD with fuzzy regression information is also proposed. The model comes into being a new approach. Applying this approach, firstly verify the conversional risk evaluation indexes by fuzzy regression technique to develop a sensitive index system. Secondly the study uses the historical risk data referring to these indexes to train the SVDD one-class classifier. Unlike previously proposed intelligent methods of risk assessment, with this model the risk level can be distinguished only by training of low risk data. The results of its application on an example show that the method is feasible for risk assessment with the fuzzy high risk data.

Support vector data description (SVDD) is a well-known technique for one-class classification problems. However, it incurs high time complexity in handling large-scale datasets. In this paper, we propose a novel approach, named K-Farthest-Neighbor-based Concept Boundary Detection (KFN-CBD), to improve the training efficiency of SVDD. KFN-CBD aims at identifying the examples lying close to the boundary of the target class, and these examples, instead of the entire dataset, are then used to learn the classifier. Extensive experiments have shown that KFN-CBD obtains substantial speedup compared to standard SVDD, and meanwhile maintains comparable accuracy as the entire dataset used.

Detection and classification are two significant tasks for industrial fault diagnosis. However, conventional methods typically treat these tasks as separate and independent problems, and necessitate a retraining process when new fault samples or classes are collected. Therefore, an incremental support vector data description scheme using Gaussian kernel function is pro-posed for industrial process fault diagnosis in a unified frame-work. In this framework, the decision boundary is updated incrementally only based on the specific original support vectors and newly collected samples. An adaptive threshold and a restructured radius are proposed to promote accuracy in the fault detection. In the classification procedure, the hyperspheres for all known classes are constructed by decision tree. The new sample that does not belong to any known class is identified as an unknown class. Without a time-consuming retraining process, the proposed diagnosis method with the incremental learning capability can synchronously achieve the fault detection and classification task. Experimental results demonstrate the effectiveness and superiority in terms of diagnosis performance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In practical industrial processes, new fault samples are collected and new fault classes emerge continually. Under such scenarios, fault diagnosis with incremental learning capability is becoming increasingly important. This work proposes a unified incremental framework for fault diagnosis based on support vector data description, which is able to achieve fault detection and classification synchronously. For fault detection, an adaptive threshold and a restructured radius are developed. For fault classification, hypersphere-shaped boundaries of all fault classes are given via the decision tree-based strategy. The proposed method is updated to include new fault samples and fault classes without dimensionality reduction or any distributional assumptions.

Illumination change is one of most important and difficult problems which prevent from applying face recognition to real applications. For solving this, we propose a method to compensate for different illumination conditions based on SVDD(Support Vector Data Description). In the proposed method, we first consider the SVDD training for the data belonging to the facial images under various illuminations, and model the data region for each illumination as the ball resulting from the SVDD training. Next, we compensate for illumination changes using feature vector projection onto the decision boundary of the SVDD ball. Finally, we obtain the pre-image under the identical illumination with input image. By repeated for each person, we can recognize a person with facial images under same illumination. We also perform the face recognition in order to verify the efficacy of proposed method.

The SVDD (support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to extend the main idea of the SVDD for the problem of pattern de-noising. Combining the projection onto the spherical decision boundary resulting from the SVDD together with a solver for the pre-image problem, we propose a new method for pattern de-noising. In the proposed method, we first solve the SVDD for the training data, then for each noisy test pattern, perform de-noising by projecting its feature vector onto the decision boundary on the feature space, and finally find the location of the de-noised pattern by obtaining the pre-image of the projection. The applicability of the proposed method is illustrated via an example dealing with noisy handwritten digits.

Deep learning with support vector data description