Just-in-time adaptive similarity component analysis in nonstationary environments

This article introduces a just-in-time adaptive nonparametric multiclass component analysis technique for application in nonstationary environments. This generative model enables adaptive similarity-based classifiers to classify time-labeled inquiry patterns with superior accuracy in low-dimensional feature space. While there are adaptive forms of feature extraction methods, which transform training patterns to low-dimensional space and/or improve classifier accuracy, they are vulnerable to nonparametric changes in data and must continuously update their parameters. In the proposed method, an optimal transformation matrix transforms time-labeled instances from the original space to a new feature space to maximize the probability of selecting the correct class label for incoming instances using similarity-based classifiers. To this end, for a given time-labeled instance, nonparametric intra-class and extra-class distributions are proposed. The proposed method is also furnished to a temporal detector to provide the most convenient time for the adaptation phase. Experimental results on real and synthesized datasets that include real and artificial changes demonstrate the performance of the proposed method in terms of accuracy and dimension reduction in dynamic environments

RFL-based customer segmentation using K-means algorithm

Customer segmentation has become crucial for the company’s survival and growth due to the rapid development of information technology (IT) and state-of-the-art databases that have facilitated the collection of customer data. Financial firms, particularly insurance companies, need to analyze these data using data mining techniques in order to identify the risk levels of their customer segments and revise the unproductive groups while retaining valuable ones. In this regard, firms have utilized clustering algorithms in conjunction with customer behavior-focused approaches, the most popular of which is RFM (recency, frequency, and monetary value). The shortcoming of the traditional RFM is that it provides a one-dimensional evaluation of customers that neglects the risk factor. Using data from 2586 insurance customers, we suggest a novel risk-adjusted RFM called RFL, where R stands for recency of policy renewal/purchase, F for frequency of policy renewal/purchase, and L for the loss ratio, which is the ratio of total incurred loss to the total earned premiums. Accordingly, customers are grouped based on the RFL variables employing the CRISP-DM and K-means clustering algorithm. In addition, further analyses, such as ANOVA as well as Duncan’s post hoc tests, are performed to ensure the quality of the results. According to the findings, the RFL performs better than the original RFM in customer differentiation, demonstrating the significant role of the risk factor in customer behavior evaluation and clustering in sectors that have to deal with customer risk

Just-in-time outdoor color discrimination using adaptive similarity-based classifier

The color recognition and identification in operation time is a critical task in color-based computer vision applications. The main problem for recognizing the real color arises when the color characteristics are changed dynamically in the life time of a system. The outdoor color models which have been addressed by some researchers have serious practical limitations to employ in real applications. Moreover, due to high fluctuations in environment illumination, using conventional classifier for discriminating colors is a complicated task. In this paper, a just-in-time and model-free solution in order to discriminate outdoor colors on data driven modality is proposed. For this purpose, adaptive similarity-based classifier is utilized to track the color's data evolution during a day

Adaptive Similarity Component Analysis in Nonparametric Dynamic Environment

Pattern classification and recognition in low-rank distance metric dealing with nonparametric changes is an underlying problem in dynamic environment applications. Data arrives from operational field in a stream model and similarity-based classification algorithms must identify them with acceptable performance. Although, there are adaptive forms of independent feature extraction methods such as principle component analysis (PCA), linear discriminant analysis (LDA) and independent component analysis (ICA) to transform the training patterns to low dimensional space and/or improve the classifiers accuracy, they suffer from nonparametric changes in data over time. This study is devoted to design a data-driven linear transformation to increase the performance of similarity-based classifiers in the presence of nonparametric changes of data over time. For this purpose, a nonparametric multiclass component analysis technique in nonstationary environments is introduced. This generative model enables adaptive similarity-based classifiers to classify time-labeled inquiry pattern with superior accuracy in a low dimensional feature space. In this thesis, an optimal transformation matrix is used to transform the time-labeled instances from original space to a new feature space in order to maximize the probability of selecting the correct class label for incoming instance by similarity-based classifiers. For this purpose, the most probable location of incoming instance for each class is estimated. Then, an optimal transformation matrix is computed by maximizing the information gain at the estimated points. By restricting the transformation matrix to a nonsquare matrix, the dimensions of feature space will be linearly reduced. Experimental results on real and synthesized datasets with real and artificial changes demonstrate the performance of the proposed method in terms of accuracy and dimension reduction in dynamic environments. In the case of real datasets, the proposed method yields 12.16% average misclassification error while the average misclassification error for five different methods GAM, TSY, NWKNN, DWM and FISH is 19.54%. Also, the results of experiments on synthesized datasets show that the proposed method yields 32.83% average misclassification error while average misclassification error of five different methods is 38.78%. From a dimensionality reduction evaluation aspect, the average misclassification error of the proposed method in low-rank feature space is 9.6% and same error rate for three other well-known feature extraction methods is 21.21%. The novelty of the proposed approach resides in the possibility to reduce the dimensions of feature space and simultaneously increase the accuracy of similarity-based classification method in an adaptive fashion in the nonparametric dynamic environment. Consequently, the proposed adaptive feature extraction technique and neighborhood-based classifier family are tightly integrated in an adaptive K-nearest neighbor classifier

A video-rate color image segmentation using adaptive and statistical membership function

The color image segmentation is a critical task in many computer vision applications. The function of segmentation is to identify homogeneous regions in an image, based on properties such as intensity, color and texture. Typically, the image segmentation algorithms in video processing system require very high computation power, so it is desirable to develop algorithms for implementation as a real-time system. This paper proposes a novel image segmentation algorithm and its real-time hardware architecture which is capable of dealing with regions color information. In this algorithm, statistical information of regions is used to create fuzzy membership functions in color model components. These membership functions characterize each segment in an image, which are updated dynamically when the image is being scanned. The histogram of color components are estimated by non-symmetric Gaussian function (NSGF). To overcome the video-rate limitation, the image is scanned in the raster fashion. Moreover, the hardware architecture of algorithm on FPGA is reported in this paper. Finally, the results of algorithm are analyzed by quantitative performance analyzers

Integration of global and local salient features for scene modeling in mobile robot applications

Many approaches have recently used global image descriptors and/or local key-point descriptors for scene understanding. In fact these approaches have suffered from lack of spatial information by using local key-point descriptors, and lack of viewpoint and local information by using global image descriptors. To overcome these problems, this paper addresses a novel image descriptor based on salient line segments (SLS), in which the global and local image features are integrated into low dimensional feature vectors. In this descriptor, low level feature maps are first computed in four scales by applying a center-surround competition technique to enhance the dominant edges and suppress small line segments. These maps are then used to extract the SLS of the image patches by creating histogram of gradients in the receptive cells. Afterwards, the global features are formed into a single vector from the coarser scales of the SLSs, and the local feature vectors are formed from the frequency of the appearance of SLSs in the finer scale. Finally, a classification step recognizes the scene of an input image by applying multi-class SVM with a Radial Bias Function (RBF) kernel. The system is performed on image sequences taken from natural scenes by a mobile agent under controlled and unexpected changes in environmental conditions. Experiments on image datasets show that the proposed method is able to classify the scenes more accurately than former methods in mobile agent environments

Brain Emotional Learning for Classification Problem

Emotional learning is new tool in the field of machine learning that the inspired from limbic system. The various models of emotional learning (BEL) have been successfully utilized in many learning problems. For example, control applications and prediction problems. In this paper a new architecture based on a brain emotional learning model that can be used in classification problem (BELC). This model is suitable for high dimensional classification applications. To evaluate the proposed method have been compare it with the Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN), Naive Bayes classifier and Brain Emotional Learning-Based Pattern Recognizer (BELPR) methods. The obtained results show the effectiveness and efficiency of the proposed method for classification problems