Social and Behavioral Sciences Commons^™

Using Cost-Sensitive Learning And Feature Selection Algorithms To Improve The Performance Of Imbalanced Classification, Fang Feng, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Xuhui Yang

Faculty of Engineering and Information Sciences - Papers: Part A

Imbalanced data problem is widely present in network intrusion detection, spam filtering, biomedical engineering, finance, science, being a challenge in many real-life data-intensive applications. Classifier bias occurs when traditional classification algorithms are used to deal with imbalanced data. As already known, the General Vector Machine (GVM) algorithm has good generalization ability, though it does not work well for the imbalanced classification. Additionally, the state-of-the-art Binary Ant Lion Optimizer (BALO) algorithm has high exploitability and fast convergence rate. Based on these facts, we have proposed in this paper a Cost-sensitive Feature selection General Vector Machine (CFGVM) algorithm based on GVM and …

Vehicle Classification By Estimation Of The Direction Angle In A Mixed Traffic Flow, Nguyen Viet Hung, Nguyen Hoang Dung, Le Chung Tran, Thang Manh Hoang, Nguyen Tien Dzung

Faculty of Engineering and Information Sciences - Papers: Part A

The application of Intelligent Transportation System (ITS) is very important in developing societies nowadays. Vehicle monitoring is one of the primary tasks of ITS, where vehicles are classified by lanes for traffic management, especially in case of a mixed flow of motorcycles and other automobiles in the transport system of Vietnam. This paper proposes a new approach in vehicle classification, which is based on evaluation of the direction angle of the first primary axis of each coming vehicle detected in the captured video sequence and map into the predetermined database to mark it as motorcycle or automobiles instead of consideration …

Video Classification Based On Spatial Gradient And Optical Flow Descriptors, Xiaolin Tang, Abdesselam Bouzerdoum, Son Lam Phung

Faculty of Engineering and Information Sciences - Papers: Part A

Feature point detection and local feature extraction are the two critical steps in trajectory-based methods for video classification. This paper proposes to detect trajectories by tracking the spatiotemporal feature points in salient regions instead of the entire frame. This strategy significantly reduces noisy feature points in the background region, and leads to lower computational cost and higher discriminative power of the feature set. Two new spatiotemporal descriptors, namely the STOH and RISTOH are proposed to describe the spatiotemporal characteristics of the moving object. The proposed method for feature point detection and local feature extraction is applied for human action recognition. …

A New Approach For Classification And Characterization Of Voltage Dips And Swells Using 3d Polarization Ellipse Parameters, Mollah R. Alam, Kashem M. Muttaqi, Abdesselam Bouzerdoum

Faculty of Engineering and Information Sciences - Papers: Part A

This paper presents a new method for classification and characterization of voltage dips and swells in electricity networks. The proposed method exploits unique signatures and parameters of three phase voltage signals extracted from the polarization ellipse in three-dimensional (3D) co-ordinates. Five ellipse parameters, which include azimuthal angle, elevation, tilt, semi-minor axis and semi-major axis, are used to classify and characterize voltage dips and swells. Seven types of voltage dips, which include a total of 19 groups of dips incorporating different kinds of balanced (three-phase dips) and unbalanced (single-phase or double-phase) dips, are identified and successfully classified using the 3D polarization …

Functional Brain Network Classification With Compact Representation Of Sice Matrices, Jianjia Zhang, Luping Zhou, Lei Wang, Wanqing Li

Faculty of Engineering and Information Sciences - Papers: Part A

Recently, sparse inverse covariance estimation (SICE) technique has been employed to model functional brain connectivity. The inverse covariance matrix (SICE matrix in short) estimated for each subject is used as a representation of brain connectivity to discriminate Alzheimers disease from normal controls. However, we observed that direct use of the SICE matrix does not necessarily give satisfying discrimination, due to its high dimensionality and the scarcity of training subjects. Looking into this problem, we argue that the intrinsic dimensionality of these SICE matrices shall be much lower, considering i) an SICE matrix resides on a Riemannian manifold of symmetric positive …

Classification Of Micro-Doppler Signatures Of Human Motions Using Log-Gabor Filters, Fok Hing Chi Tivive, Son Lam Phung, Abdesselam Bouzerdoum

Faculty of Engineering and Information Sciences - Papers: Part A

In recent years, Doppler radar has been used as a sensing modality for human gait recognition, due to its ability to operate in adverse weather and penetrate opaque obstacles. Doppler radar captures not only the speed of the target, but also the micro-motions of its moving parts. These micro-motions induce frequency modulations that can be used to characterise the target movements. However, a major challenge in Doppler signal processing is to extract discriminative features from the radar returns for target classification. This study presents a feature extraction method for classification of human motions from the micro-Doppler radar signal. The proposed …

Discriminative Sparse Inverse Covariance Matrix: Application In Brain Functional Network Classification, Luping Zhou, Lei Wang, Philip O. Ogunbona

Faculty of Engineering and Information Sciences - Papers: Part A

Recent studies show that mental disorders change the functional organization of the brain, which could be investigated via various imaging techniques. Analyzing such changes is becoming critical as it could provide new biomarkers for diagnosing and monitoring the progression of the diseases. Functional connectivity analysis studies the covary activity of neuronal populations in different brain regions. The sparse inverse covariance estimation (SICE), also known as graphical LASSO, is one of the most important tools for functional connectivity analysis, which estimates the interregional partial correlations of the brain. Although being increasingly used for predicting mental disorders, SICE is basically a generative …

Multiple Kernel Learning In The Primal For Multimodal Alzheimer's Disease Classification, Fayao Liu, Luping Zhou, Chunhua Shen, Jianping Yin

Faculty of Engineering and Information Sciences - Papers: Part A

To achieve effective and efficient detection of Alzheimer's disease (AD), many machine learning methods have been introduced into this realm. However, the general case of limited training samples, as well as different feature representations typically makes this problem challenging. In this work, we propose a novel multiple kernel learning framework to combine multi-modal features for AD classification, which is scalable and easy to implement. Contrary to the usual way of solving the problem in the dual, we look at the optimization from a new perspective. By conducting Fourier transform on the Gaussian kernel, we explicitly compute the mapping function, which …

Hep-2 Cell Image Classification With Multiple Linear Descriptors, Lingqiao Liu, Lei Wang

Faculty of Engineering and Information Sciences - Papers: Part A

The automatic classification of the HEp-2 cell stain patterns from indirect immunofluorescence images has attracted much attention recently. As an image classification problem, it can be well solved by the state-of-the-art bag-of-features (BoF) model as long as a suitable local descriptor is known. Unfortunately, for this special task, we have very limited knowledge of such a descriptor. In this paper, we explore the possibility of automatically learning the descriptor from the image data itself. Specifically, we assume that a local patch can be well described by a set of linear projections performed on its pixel values. Based on this assumption, …

Automated Authorship Attribution Using Advanced Signal Classification Techniques, Maryam Ebrahimpour, Talis J. Putnins, Matthew J. Berryman, Andrew Allison, Brian W-H Ng, Derek Abbott

Faculty of Engineering and Information Sciences - Papers: Part A

In this paper, we develop two automated authorship attribution schemes, one based on Multiple Discriminant Analysis (MDA) and the other based on a Support Vector Machine (SVM). The classification features we exploit are based on word frequencies in the text. We adopt an approach of preprocessing each text by stripping it of all characters except a-z and space. This is in order to increase the portability of the software to different types of texts. We test the methodology on a corpus of undisputed English texts, and use leave-one-out cross validation to demonstrate classification accuracies in excess of 90%. We further …

Influence Of Rock Depth On Seismic Site Classification For Shallow Bedrock Regions, P Anbazhagan, M. Neaz Sheikh, Aditya Parihar

Faculty of Engineering and Information Sciences - Papers: Part A

Seismic site classifications are used to represent site effects for estimating hazard parameters (response spectral ordinates) at the soil surface. Seismic site classifications have generally been carried out using average shear wave velocity and/or standard penetration test n-values of top 30-m soil layers, according to the recommendations of the National Earthquake Hazards Reduction Program (NEHRP) or the International Building Code (IBC). The site classification system in the NEHRP and the IBC is based on the studies carried out in the United States where soil layers extend up to several hundred meters before reaching any distinct soil-bedrock interface and may not …

A Classification Theorem For Helfrich Surfaces, James Mccoy, Glen Wheeler

Faculty of Engineering and Information Sciences - Papers: Part A

In this paper we study the functional W , which is the the sum of the Willmore energy, weighted surface area, and weighted volume, for surfaces immersed in R^3. This coincides with the Helfrich functional with zero `spontaneous curvature'. Our main result is a complete classification of all smooth immersed critical points of the functional with nonnegative surface area weight and small L^2 norm of tracefree curvature. In particular we prove the non-existence of critical points of the functional for which the surface area and enclosed volume are positively weighted.

An Image-Based Approach For Classification Of Human Micro-Doppler Radar Signatures, Fok Hing Chi Tivive, Son Lam Phung, Abdesselam Bouzerdoum

Faculty of Engineering and Information Sciences - Papers: Part A

With the advances in radar technology, there is an increasing interest in automatic radar-based human gait identification. This is because radar signals can penetrate through most dielectric materials. In this paper, an image-based approach is proposed for classifying human micro-Doppler radar signatures. The time-varying radar signal is first converted into a time-frequency representation, which is then cast as a two-dimensional image. A descriptor is developed to extract micro-Doppler features from local time-frequency patches centered along the torso Doppler frequency. Experimental results based on real data collected from a 24-GHz Doppler radar showed that the proposed approach achieves promising classification performance.

Sparse Representation Of Gpr Traces With Application To Signal Classification, Wenbin Shao, Abdesselam Bouzerdoum, Son Lam Phung

Faculty of Engineering and Information Sciences - Papers: Part A

Sparse representation (SR) models a signal with a small number of elementary waves using an overcomplete dictionary. It has been employed for a wide range of signal and image processing applications, including denoising, deblurring, and compression. In this paper, we present an adaptive SR method for modeling and classifying ground penetrating radar (GPR) signals. The proposed method decomposes each GPR trace into elementary waves using an adaptive Gabor dictionary. The sparse decomposition is used to extract salient features for SR and classification of GPR signals. Experimental results on real-world data show that the proposed sparse decomposition achieves efficient signal representation …

Computer Aided Decision Support System For Cervical Cancer Classification, - Rahmadwati, Golshah Naghdy, Montserrat B. Ros, Catherine Todd

Faculty of Engineering and Information Sciences - Papers: Part A

Conventional analysis of a cervical histology image, such a pap smear or a biopsy sample, is performed by an expert pathologist manually. This involves inspecting the sample for cellular level abnormalities and determining the spread of the abnormalities. Cancer is graded based on the spread of the abnormal cells. This is a tedious, subjective and timeconsuming process with considerable variations in diagnosis between the experts. This paper presents a computer aided decision support system (CADSS) tool to help the pathologists in their examination of the cervical cancer biopsies. The main aim of the proposed CADSS system is to identify abnormalities …

Scene Segmentation And Pedestrian Classification From 3-D Range And Intensity Images, Xue Wei, Son Lam Phung, Abdesselam Bouzerdoum

Faculty of Engineering and Information Sciences - Papers: Part A

This paper proposes a new approach to classify obstacles using a time-of-flight camera, for applications in assistive navigation of the visually impaired. Combining range and intensity images enables fast and accurate object segmentation, and provides useful navigation cues such as distances to the nearby obstacles and obstacle types. In the proposed approach, a 3-D range image is first segmented using histogram thresholding and mean-shift grouping. Then Fourier and GIST descriptors are applied on each segmented object to extract shape and texture features. Finally, support vector machines are used to recognize the obstacles. This paper focuses on classifying pedestrian and non-pedestrian …

A Kernel Fuzzy C-Means Clustering-Based Fuzzy Support Vector Machine Algorithm For Classification Problems With Outliers Or Noises, Xiaowei Yang, Guangquan Zhang, Jie Lu, Jun Ma

Faculty of Engineering and Information Sciences - Papers: Part A

The support vector machine (SVM) has provided higher performance than traditional learning machines and has been widely applied in real-world classification problems and nonlinear function estimation problems. Unfortunately, the training process of the SVM is sensitive to the outliers or noises in the training set. In this paper, a common misunderstanding of Gaussian-function-based kernel fuzzy clustering is corrected, and a kernel fuzzy c-means clustering-based fuzzy SVM algorithm (KFCM-FSVM) is developed to deal with the classification problems with outliers or noises. In the KFCM-FSVM algorithm, we first use the FCM clustering to cluster each of two classes from the training set …

Hippocampal Shape Classification Using Redundancy Constrained Feature Selection, Luping Zhou, Lei Wang, Chunhua Shen, Nick Barnes

Faculty of Engineering and Information Sciences - Papers: Part A

Landmark-based 3D hippocampal shape classification involves high-dimensional descriptor space, many noisy and redundant features, and a very small number of training samples. Feature selection becomes critical in this situation, because it not only improves classification performance, but also identifies the regions that contribute more to shape discrimination. This work identifies the drawbacks of SVM-RFE, and proposes a novel class-separability-based feature selection approach to overcome them. We formulate feature selection as a constrained integer optimization and develop a new algorithm to efficiently and optimally solve this problem. Theoretical analysis and experimental study on both synthetic data and real hippocampus data demonstrate …

Feature Subset Selection For Multi-Class Svm Based Image Classification, Lei Wang

Faculty of Engineering and Information Sciences - Papers: Part A

Multi-class image classification can benefit much from feature subset selection. This paper extends an error bound of binary SVMs to a feature subset selection criterion for the multi-class SVMs. By minimizing this criterion, the scale factors assigned to each feature in a kernel function are optimized to identify the important features. This minimization problem can be efficiently solved by gradient-based search techniques, even if hundreds of features are involved. Also, considering that image classification is often a small sample problem, the regularization issue is investigated for this criterion, showing its robustness in this situation. Experimental study on multiple benchmark image …

Improving Adaboost For Classification On Small Training Sample Sets With Active Learning, Lei Wang, Xuchun Li, Eric Sung

Faculty of Engineering and Information Sciences - Papers: Part A

Recently, AdaBoost has been widely used in many computer vision applications and has shown promising results. However, it is also observed that its classification performance is often poor when the size of the training sample set is small. In certain situations, there may be many unlabelled samples available and labelling them is costly and time-consuming. Thus it is desirable to pick a few good samples to be labelled. The key is how. In this paper, we integrate active learning with AdaBoost to attack this problem. The principle idea is to select the next unlabelled sample base on it being at …

Classification Theorems For The C*-Algebras Of Graphs With Sinks, Iain Raeburn, Mark Tomforde, Dana Williams

Faculty of Engineering and Information Sciences - Papers: Part A

We consider graphs E which have been obtained by adding one or more sinks to a fixed directed graph G. We classify the C^*-algebra of E up to a very strong equivalence relation, which insists, loosely speaking, that C^*(G) is kept fixed. The main invariants are vectors W_E: G⁰ → which describe how the sinks are attached to G; more precisely, the invariants are the classes of the W_E in the cokernel of the map A – I, where A is the adjacency matrix of the graph …

A Classification Of Intersection Type Systems, Martin W. Bunder

Faculty of Engineering and Information Sciences - Papers: Part A

The first system of intersection types. Coppo and Dezani [3], extended simple types to include intersections and added intersection introduction and elimination rules ((ΛI ) and (ΛE) ) to the type assignment system. The major advantage of these new types was that they were invariant under β-equality, later work by Barendregt, Coppo and Dezani [1], extended this to include an (η) rule which gave types invariant under βη-reduction.

Urzyczyn proved in [6] that for both these systems it is undecidable whether a given intersection type is empty. Kurata and Takahashi however have shown in [5] …

A Distribution-Based Face/Nonface Classification Technique, Son Lam Phung, Douglas Chai, Abdesselam Bouzerdoum

Faculty of Engineering and Information Sciences - Papers: Part A

The core element of many existing approaches to face detection is the classification algorithm that determines if a sub-image of an input image contains a face pattern. In this paper, we present a novel and effective distribution-based face/non-face classification technique that detects frontal face patterns with possible in-plane rotation. A 15x15 input sub-image is first processed by a color filter, which verifies the presence of human skin color in the sub-image. Then, the intensity image is extracted from the identified skin color sub-image and converted into a vector in a high-dimensional space (R²²⁵). Principal component analysis is …

Neural Network Classification And Prior Class Probabilities, Steve Lawrence, Ian Burns, Andrew Back, Ah Chung Tsoi, C Lee Giles

Faculty of Engineering and Information Sciences - Papers: Part A

A commonly encountered problem in MLP (multi-layer perceptron) classification problems is related to the prior probabilities of the individual classes - if the number of training examples that correspond to each class varies significantly between the classes, then it may be harder for the network to learn the rarer classes in some cases. Such practical experience does not match theoretical results which show that MLPs approximate Bayesian a posteriori probabilities (independent of the prior class probabilities). Our investigation of the problem shows that the difference between the theoretical and practical results lies with the assumptions made in the theory (accurate …