Tingting Mu

Classification of Breast Masses Using Selected Shape, Edge-sharpness, and Texture Features with Linear and Kernel-based Classifiers

Breast masses due to benign disease and malignant tumors related to breast cancer differ in terms of shape, edge-sharpness, and texture characteristics. In this study, we evaluate a set of 22 features including 5 shape factors, 3 edge-sharpness measures, and 14 texture features computed from 111 regions in mammograms, with 46 regions related to malignant tumors and 65 to benign masses. Feature selection is performed by a genetic algorithm based on several criteria, such as alignment of the kernel with the target function, class separability, and normalized distance. Fisher’s linear discriminant analysis, the support vector machine (SVM), and our strict two-surface proximal (S2SP) classifier, as well as their corresponding kernel-based nonlinear versions, are used in the classification task with the selected features. The nonlinear classification performance of kernel Fisher’s discriminant analysis, SVM, and S2SP, with the Gaussian kernel, reached 0.95 in terms of the area under the receiver operating characteristics curve. The results indicate that improvement in classification accuracy may be gained by using selected combinations of shape, edge-sharpness, and texture features.

Multiclass Classification Based on Extended Support Vector Data Description

We propose two variations of the support vector data description (SVDD) with negative samples (NSVDD) that learn a closed spherically shaped boundary around a set of samples in the target class by involving different forms of slack vectors, including the two-norm NSVDD and nu-NSVDD. We extend the NSVDDs to solve the multiclass classification problems based on the distances between the samples and the centers of the learned spherically shaped boundaries in a kernel-defined feature space by using a combination of linear discriminant analysis (LDA) and nearest-neighbor (NN) rule. Extensive simulations are developed with one real-world data set on the automatic monitoring of roller bearings with vibration signals and eight benchmark data sets for both binary and multiclass classification. The benchmark testing results show that our proposed methods provide lower classification error rates and smaller standard deviations with the cross-validation procedure. The two-norm NSVDD with the LDA-NN rule recorded a test accuracy of 100.0% for the binary fault detection of roller bearings and 99.9% for the multiclass classification of roller bearings under six conditions.

Breast cancer detection from FNA using SVM with different parameter tuning systems and SOM--RBF classifier

Abstract In this paper, we consider the benefits of applying support vector machines (SVMs), radial basis function (RBF) networks, and self-organizing maps (SOMs) for breast cancer detection. The Wisconsin diagnosis breast cancer (WDBC) dataset is used in the classification experiments; the dataset was generated from fine needle aspiration (FNA) samples through image processing. The 1-norm C-SVM ( L 1 -SVM), 2-norm C-SVM ( L 2 -SVM), and υ -SVM classifiers are applied, for which the grid search based on span error estimate (GSSEE), gradient descent based on validation error estimate (GDVEE), and gradient descent based on span error estimate (GDSEE) are developed to improve the detection accuracy. The gradient descent (GD) tuning method based on the span error estimate (SEE) is employed for the L 2 -SVM classifier because of its reachable smooth nonlinearity. Such a GDSEE tuning system also has the advantage of saving available samples for the training procedure. The SOM–RBF classifier is developed to improve the performance of only the SOM learning procedure based on distance comparison, in which the RBF network is employed to process the clustering result obtained by the SOM. Experimental results demonstrate that SVM classifiers with the proposed automatic parameter tuning systems and the SOM–RBF classifier can be efficient tools for breast cancer detection, with the detection accuracy up to 98 % .

Context-Aware and Energy-Driven Route Optimization for Fully Electric Vehicles via Crowdsourcing

Route planning for fully electric vehicles (FEVs) must take energy efficiency into account due to limited battery capacity and time-consuming recharging. In addition, the planning algorithm should allow for negative energy costs in the road network due to regenerative braking, which is a unique feature of FEVs. In this paper, we propose a framework for energy-driven and context-aware route planning for FEVs. It has two novel aspects: 1) It is context aware, i.e., the framework has access to real-time traffic data for routing cost estimation; and it is energy driven, i.e., both time and energy efficiency are accounted for; which implies a biobjective nature of the optimization. In addition, in the case of insufficient energy on board, an optimal detour via recharge points is computed. Our main contributions to address these issues can be highlighted as follows: A vehicle-to-vehicle (V2V) communication protocol is proposed to realize the context awareness, and we replace the original biobjective form of optimality with two single-objective forms and propose a constrained A* ( CA*) algorithm to find the solutions. The algorithm maintains a Pareto front while it confines its search by energy constraints. The best recharging detour can be also found using the algorithm. We first compared the performance of the CA* algorithm with other algorithms. We then evaluate the impact of the context awareness on road traffic by simulations using a realistic road network regarding different forms of optimality. Finally, we show that the CA* algorithm can effectively produce optimal recharging detours.

Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings

Unsupervised Cross-domain Sentiment Classification is the task of adapting a sentiment classifier trained on a particular domain (source domain), to a different domain (target domain), without requiring any labeled data for the target domain. By adapting an existing sentiment classifier to previously unseen target domains, we can avoid the cost for manual data annotation for the target domain. We model this problem as embedding learning, and construct three objective functions that capture: (a) distributional properties of pivots (i.e., common features that appear in both source and target domains), (b) label constraints in the source domain documents, and (c) geometric properties in the unlabeled documents in both source and target domains. Unlike prior proposals that first learn a lower-dimensional embedding independent of the source domain sentiment labels, and next a sentiment classifier in this embedding, our joint optimisation method learns embeddings that are sensitive to sentiment classification. Experimental results on a benchmark dataset show that by jointly optimising the three objectives we can obtain better performances in comparison to optimising each objective function separately, thereby demonstrating the importance of task-specific embedding learning for cross-domain sentiment classification. Among the individual objective functions, the best performance is obtained by (c). Moreover, the proposed method reports cross-domain sentiment classification accuracies that are statistically comparable to the current state-of-the-art embedding learning methods for cross-domain sentiment classification.

Gait recognition: highly unique dynamic plantar pressure patterns among 104 individuals

Everyones walking style is unique, and it has been shown that both humans and computers are very good at recognizing known gait patterns. It is therefore unsurprising that dynamic foot pressure patterns, which indirectly reflect the accelerations of all body parts, are also unique, and that previous studies have achieved moderate-to-high classification rates (CRs) using foot pressure variables. However, these studies are limited by small sample sizes (n < 30), moderate CRs (CR ≃ 90%), or both. Here we show, using relatively simple image processing and feature extraction, that dynamic foot pressures can be used to identify n = 104 subjects with a CR of 99.6 per cent. Our key innovation was improved and automated spatial alignment which, by itself, improved CR to over 98 per cent, a finding that pointedly emphasizes inter-subject pressure pattern uniqueness. We also found that automated dimensionality reduction invariably improved CRs. As dynamic pressure data are immediately usable, with little or no pre-processing required, and as they may be collected discreetly during uninterrupted gait using in-floor systems, foot pressure-based identification appears to have wide potential for both the security and health industries.

Proximity-Based Frameworks for Generating Embeddings from Multi-Output Data

This paper is about supervised and semi-supervised dimensionality reduction (DR) by generating spectral embeddings from multi-output data based on the pairwise proximity information. Two flexible and generic frameworks are proposed to achieve supervised DR (SDR) for multilabel classification. One is able to extend any existing single-label SDR to multilabel via sample duplication, referred to as MESD. The other is a multilabel design framework that tackles the SDR problem by computing weight (proximity) matrices based on simultaneous feature and label information, referred to as MOPE, as a generalization of many current techniques. A diverse set of different schemes for label-based proximity calculation, as well as a mechanism for combining label-based and feature-based weight information by considering information importance and prioritization, are proposed for MOPE. Additionally, we summarize many current spectral methods for unsupervised DR (UDR), single/multilabel SDR, and semi-supervised DR (SSDR) and express them under a common template representation as a general guide to researchers in the field. We also propose a general framework for achieving SSDR by combining existing SDR and UDR models, and also a procedure of reducing the computational cost via learning with a target set of relation features. The effectiveness of our proposed methodologies is demonstrated with experiments with document collections for multilabel text categorization from the natural language processing domain.

Classification of breast masses via nonlinear transformation of features based on a kernel matrix

We propose methods to perform a certain nonlinear transformation of features based on a kernel matrix, before the classification step, aiming to improve the discriminating power of the comparatively weak edge-sharpness and texture features of breast masses in mammograms, and seek better incorporation of features representing different radiological characteristics than shape features only. Kernel principal component analysis (KPCA) is applied to improve the discriminating power of each single feature in an expanded feature space and the discriminating capability of different feature combinations in other transformed, more informative, lower-dimensional feature spaces. A kernel partial least squares (KPLS) method is developed to derive score vectors for a shape feature set, and an edge-sharpness and texture feature set, respectively, with minimal covariance between each other, to help in achieving improved diagnosis using multiple radiological characteristics of breast masses. Fisher’s linear discriminant analysis (FLDA) is employed to evaluate the classification capability of the transformed features. The methods were tested with a set of 57 regions in mammograms, of which 20 are related to malignant tumors and 37 to benign masses, represented using five shape features, three edge-sharpness features, and 14 texture features. The classification performance of the edge-sharpness and texture features, via KPCA transformation, was significantly improved from 0.75 to 0.85 in terms of the area under the receiver operating characteristics curve (Az). The classification performance of all of the shape, edge-sharpness, and texture features, via KPLS transformation, was improved from 0.95 to 1.0 in Az value.

Automatic Induction of Projection Pursuit Indices

Projection techniques are frequently used as the principal means for the implementation of feature extraction and dimensionality reduction for machine learning applications. A well established and broad class of such projection techniques is the projection pursuit (PP). Its core design parameter is a projection index, which is the driving force in obtaining the transformation function via optimization, and represents in an explicit or implicit way the users perception of the useful information contained within the datasets. This paper seeks to address the problem related to the design of PP index functions for the linear feature extraction case. We achieve this using an evolutionary search framework, capable of building new indices to fit the properties of the available datasets. The high expressive power of this framework is sustained by a rich set of function primitives. The performance of several PP indices previously proposed by human experts is compared with these automatically generated indices for the task of classification, and results show a decrease in the classification errors.

Stochastic Multiview Hashing for Large-Scale Near-Duplicate Video Retrieval

Near-duplicate video retrieval (NDVR) has been a significant research task in multimedia given its high impact in applications, such as video search, recommendation, and copyright protection. In addition to accurate retrieval performance, the exponential growth of online videos has imposed heavy demands on the efficiency and scalability of the existing systems. Aiming at improving both the retrieval accuracy and speed, we propose a novel stochastic multiview hashing algorithm to facilitate the construction of a large-scale NDVR system. Reliable mapping functions, which convert multiple types of keyframe features, enhanced by auxiliary information such as video-keyframe association and ground truth relevance to binary hash code strings, are learned by maximizing a mixture of the generalized retrieval precision and recall scores. A composite Kullback-Leibler divergence measure is used to approximate the retrieval scores, which aligns stochastically the neighborhood structures between the original feature and the relaxed hash code spaces. The efficiency and effectiveness of the proposed method are examined using two public near-duplicate video collections and are compared against various classical and state-of-the-art NDVR systems.