Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval

Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training set, 2) SVM's optimal hyperplane may be biased when the positive feedback samples are much less than the negative feedback samples, and 3) overfitting happens because the number of feature dimensions is much higher than the size of the training set. In this paper, we develop a mechanism to overcome these problems. To address the first two problems, we propose an asymmetric bagging-based SVM (AB-SVM). For the third problem, we combine the random subspace method and SVM for relevance feedback, which is named random subspace SVM (RS-SVM). Finally, by integrating AB-SVM and RS-SVM, an asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve these three problems and further improve the relevance feedback performance

Two-Stage Fuzzy Multiple Kernel Learning Based on Hilbert-Schmidt Independence Criterion

© 1993-2012 IEEE. Multiple kernel learning (MKL) is a principled approach to kernel combination and selection for a variety of learning tasks, such as classification, clustering, and dimensionality reduction. In this paper, we develop a novel fuzzy multiple kernel learning model based on the Hilbert-Schmidt independence criterion (HSIC) for classification, which we call HSIC-FMKL. In this model, we first propose an HSIC Lasso-based MKL formulation, which not only has a clear statistical interpretation that minimum redundant kernels with maximum dependence on output labels are found and combined, but also enables the global optimal solution to be computed efficiently by solving a Lasso optimization problem. Since the traditional support vector machine (SVM) is sensitive to outliers or noises in the dataset, fuzzy SVM (FSVM) is used to select the prediction hypothesis once the optimal kernel has been obtained. The main advantage of FSVM is that we can associate a fuzzy membership with each data point such that these data points can have different effects on the training of the learning machine. We propose a new fuzzy membership function using a heuristic strategy based on the HSIC. The proposed HSIC-FMKL is a two-stage kernel learning approach and the HSIC is applied in both stages. We perform extensive experiments on real-world datasets from the UCI benchmark repository and the application domain of computational biology which validate the superiority of the proposed model in terms of prediction accuracy

A rest time-based prognostic framework for state of health estimation of lithium-ion batteries with regeneration phenomena

State of health (SOH) prognostics is significant for safe and reliable usage of lithium-ion batteries. To accurately predict regeneration phenomena and improve long-term prediction performance of battery SOH, this paper proposes a rest time-based prognostic framework (RTPF) in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time. In this framework, SOH values of regeneration cycles, the number of cycles in regeneration regions and global degradation trends are extracted from raw SOH time series and predicted respectively, and then the three sets of prediction results are integrated to calculate the final overall SOH prediction values. Regeneration phenomena can be found by support vector machine and hyperplane shift (SVM-HS) model by detecting long beginning time intervals. Gaussian process (GP) model is utilized to predict the global degradation trend, and nonlinear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated through experimental data from the degradation tests of lithium-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framewor

DESIGN AND SIMULATION OF AN EFFICIENT MODEL FOR CREDIT CARDS FRAUD DETECTION

In this study a model which can improve the accuracy and reliability of credit card fraud detection was proposed. This is with a few to mitigating contentious issues regarding online transaction of credit card, such as  amount of transactions that have resulted in payment default and the number of credit card fraud cases that have been recorded, all of which have put the economy in jeopardy.   To address this challenge,sample dataset was sourced from online repository database of Kaggle. The feature extraction on the data was performed using Principal Component Analysis (PCA). The credit card fraud detection model was designed using Neuro-fuzzy logic technique, clustering was done using Hierarchical Density Based Spatial Clustering of Application with Noise (HDBSCAN) .The simulation of the proposed model was done in Python programming environment.The performance evaluation of the model was carried out by comparing the proposed model with Neuro-Fuzzy (NF) technique using performance metrics such as precision, recall, F1-score and accuracy.  The simulation result showed that the proposed model (NF + HDBSCAN) had precision of 98.75%, recall of 98.70%, F1-Score of 97.65% and accuracy 99.75% . NF had Precision of 94.60%, recall of 94.50%, F1-Score of 95.50% and accuracy 95.70% using training dataset. Likewise, when test dataset were used, the proposed (NF + HDBSCAN) had precision of 93.50%, recall of 95.50%, F1-Score of 94.50% and accuracy 95.50%. NF had Precision of 92.50%, recall of 93.00%, F1-Score of 94.00% and accuracy 93.50%.  The simulation results of the proposed model was viable, reliable and showed possibility of being designed as module which could be  integrated into the existing credit card design for lowering fraud rate and assisting fraud investigators