Conventional Classification Algorithms Research Articles

When Collective Knowledge Meets Crowd Knowledge in a Smart City: A Prediction Method Combining Open Data Keyword Analysis and Case-Based Reasoning.

One of the significant issues in a smart city is maintaining a healthy environment. To improve the environment, huge amounts of data are gathered, manipulated, analyzed, and utilized, and these data might include noise, uncertainty, or unexpected mistreatment of the data. In some datasets, the class imbalance problem skews the learning performance of the classification algorithms. In this paper, we propose a case-based reasoning method that combines the use of crowd knowledge from open source data and collective knowledge. This method mitigates the class imbalance issues resulting from datasets, which diagnose wellness levels in patients suffering from stress or depression. We investigate effective ways to mitigate class imbalance issues in which the datasets have a higher proportion of one class over another. The results of this proposed hybrid reasoning method, using a combination of crowd knowledge extracted from open source data (i.e., a Google search, or other publicly accessible source) and collective knowledge (i.e., case-based reasoning), were that it performs better than other traditional methods (e.g., SMO, BayesNet, IBk, Logistic, C4.5, and crowd reasoning). We also demonstrate that the use of open source and big data improves the classification performance when used in addition to conventional classification algorithms.

Convolutional neural network for classifying space target of the same shape by using RCS time series

Warhead and decoy classification is one of the most important and difficult technical problems in ballistic missile defence. The conventional methods extract features from the measured data and employ some classification algorithms. However, it is hard to extract all the information embedded in the raw data, and there might be contradictory features lowering the classification ability. A one-dimensional convolutional neural network structure named RCSnet was proposed to classify the warhead and decoy targets of the same shape in midcourse, which directly utilises the radar cross-section (RCS) time series. It was compared with 5 conventional classification algorithms which used 26 selected features on simulation dataset, and it outperformed them in both classification performance and predicting speed. Different training algorithms and networks of the RCSnet structure with different filter numbers were explored for better utilising the RCSnet.

Biomedical Text Categorization Based on Ensemble Pruning and Optimized Topic Modelling.

Text mining is an important research direction, which involves several fields, such as information retrieval, information extraction, and text categorization. In this paper, we propose an efficient multiple classifier approach to text categorization based on swarm-optimized topic modelling. The Latent Dirichlet allocation (LDA) can overcome the high dimensionality problem of vector space model, but identifying appropriate parameter values is critical to performance of LDA. Swarm-optimized approach estimates the parameters of LDA, including the number of topics and all the other parameters involved in LDA. The hybrid ensemble pruning approach based on combined diversity measures and clustering aims to obtain a multiple classifier system with high predictive performance and better diversity. In this scheme, four different diversity measures (namely, disagreement measure, Q-statistics, the correlation coefficient, and the double fault measure) among classifiers of the ensemble are combined. Based on the combined diversity matrix, a swarm intelligence based clustering algorithm is employed to partition the classifiers into a number of disjoint groups and one classifier (with the highest predictive performance) from each cluster is selected to build the final multiple classifier system. The experimental results based on five biomedical text benchmarks have been conducted. In the swarm-optimized LDA, different metaheuristic algorithms (such as genetic algorithms, particle swarm optimization, firefly algorithm, cuckoo search algorithm, and bat algorithm) are considered. In the ensemble pruning, five metaheuristic clustering algorithms are evaluated. The experimental results on biomedical text benchmarks indicate that swarm-optimized LDA yields better predictive performance compared to the conventional LDA. In addition, the proposed multiple classifier system outperforms the conventional classification algorithms, ensemble learning, and ensemble pruning methods.

Hyperspectral Image Classification With Imbalanced Data Based on Orthogonal Complement Subspace Projection

Conventional classification algorithms have shown great success for balanced classes. In remote sensing applications, it is often the case that classes are imbalanced. This paper proposes a novel solution to solve the problem of imbalanced training samples in hyperspectral image classification. It consists of two parts: one is for large-size sample sets and the other is for small-size sets. Specifically, an algorithm based on the orthogonal complement subspace projection (OCSP) is proposed to select samples from large-size classes, and an algorithm also based on OCSP is proposed to create artificial samples for small-size ones. The impact on representation-based classifiers, i.e., sparse and collaborative representation classifiers and traditional classifiers (e.g., support vector machine), is investigated. Experimental results demonstrate that the proposed solution can outperform other existing solutions in the literature.

Identifying tweets of personal health experience through word embedding and LSTM neural network

BackgroundAs Twitter has become an active data source for health surveillance research, it is important that efficient and effective methods are developed to identify tweets related to personal health experience. Conventional classification algorithms rely on features engineered by human domain experts, and engineering such features is a challenging task and requires much human intelligence. The resultant features may not be optimal for the classification problem, and can make it challenging for conventional classifiers to correctly predict personal experience tweets (PETs) due to the various ways to express and/or describe personal experience in tweets. In this study, we developed a method that combines word embedding and long short-term memory (LSTM) model without the need to engineer any specific features. Through word embedding, tweet texts were represented as dense vectors which in turn were fed to the LSTM neural network as sequences.ResultsStatistical analyses of the results of 10-fold cross-validations of our method and conventional methods indicate that there exist significant differences (p < 0.01) in performance measures of accuracy, precision, recall, F1-score, and ROC/AUC, demonstrating that our approach outperforms the conventional methods in identifying PETs.ConclusionWe presented an efficient and effective method of identifying health-related personal experience tweets by combining word embedding and an LSTM neural network. It is conceivable that our method can help accelerate and scale up analyzing textual data of social media for health surveillance purposes, because of no need for the laborious and costly process of engineering features.

Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm.

Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters) were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.

Optimised multikernels based extreme learning machine for face recognition

Extreme learning machine (ELM) is a simple and efficient learning algorithm for regression and classification problems, because of its better generalisation performance. Though ELM is fast due to its mild optimisation constraints, the classification accuracy of basic ELM and its variants depend on the choice of kernel. Thorough investigations need to be done on conventional kernel-based classification algorithms to optimise the selection of kernels, which is essential for the good performance of a classifier. As different kernels have different feature extraction capability, a general framework to formulate an optimal kernel based on the concept of multiple kernels learning has been described in this paper. In the proposed approach, linearly combined base kernels have been used as the kernel matrix function which is non-static in nature. The values of coefficients used for combination can be tuned according to the application. Experiments on various face image datasets clearly reveal that the adoption of proposed framework can achieve better results than ELM and kernel ELM.

Multiple kernel approach to semi-supervised fuzzy clustering algorithm for land-cover classification

Clustering is used to detect sound structures or patterns in a dataset in which objects positioned within the same cluster exhibit a substantial level of similarity. In numerous clustering problems, patterns is not easily separable due to the highly complex shaped data. In the previous studies, kernel-based methods have exhibited the effectiveness to partition such data. In this paper, we proposed a semi-supervised clustering method based fuzzy c-means algorithm using multiple kernel technique, called SMKFCM, in which the rudimentary centroids are directly used to the calculating process of centroids. The SMKFCM algorithm is on the basis of combining the labeled and unlabeled data together to improve performance. We used the labeled patterns to calculate the centrality of clusters considered as the rudimentary centroids which are added into the objective functions. The SMKFCM algorithm can be applied to both clustering and classification problems. The experimental results show that SMKFCM algorithm can improve significantly the classification accuracy which comes from comparison with a conventional classification or clustering algorithms such as semi-supervised kernel fuzzy c-means (S2KFCM), semi-supervised fuzzy c-means (SFCM) and Self-trained semi-supervised SVM algorithm (PS3VM).

EVALUATION OF MULTIPLE KERNEL LEARNING ALGORITHMS FOR CROP MAPPING USING SATELLITE IMAGE TIME-SERIES DATA

Abstract. Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.

Instance-based classification with Ant Colony Optimization

Instance-based learning (IBL) methods predict the class label of a new instance based directly on the distance between the new unlabeled instance and each labeled instance in the training set, without constructing a classification model in the training phase. In this paper, we introduce a novel class-based feature weighting technique, in the context of instance-based distance methods, using the Ant Colony Optimization meta-heuristic. We address three different approaches of instance-based classification: k-Nearest Neighbours, distance-based Nearest Neighbours, and Gaussian Kernel Estimator. We present a multi-archive adaptation of the ACO? algorithm and apply it to the optimization of the key parameter in each IBL algorithm and of the class-based feature weights. We also propose an ensemble of classifiers approach that makes use of the archived populations of the ACO? algorithm. We empirically evaluate the performance of our proposed algorithms on 36 benchmark datasets, and compare them with conventional instance-based classification algorithms, using various parameter settings, as well as with a state-of-the-art coevolutionary algorithm for instance selection and feature weighting for Nearest Neighbours classifiers.

ASSESSMENT OF LIBRARY USERS’ FEEDBACK USING MODIFIED MULTILAYER PERCEPTRON NEURAL NETWORKS

An attempt has been made to evaluate the feedbacks of library users of four different libraries by using neural network based data mining techniques. This paper presents the results of a survey of users' satisfactory level on four different libraries. The survey has been conducted among the users of four libraries of educational institutions of Kovai Medical Center Research and Educational Trust. Data were collected through questionnaires. Artificial neural network based data mining techniques are proposed and applied to assess the libraries in terms of level of satisfaction of users. In order to assess the users' satisfaction level, two neural network techniques: Modified Multilayer Perceptron Network-Supervised and Modified Multilayer Perceptron Network-Unsupervised are proposed. The proposed techniques are compared with the conventional classification algorithm Multilayer Perceptron Neural Network and found better in overall performance. It is found that the quality of service provided by the libraries is highly good and users are highly satisfied with various aspects of library service. The Arts and Science College Library secured the maximum percent in terms of user satisfaction. This shows that the users' satisfaction of ASCL is better than the other libraries. This study provides an insight into the actual quality and satisfactory level of users of libraries after proper assessment. It is strongly expected that the results will help library authorities to enhance services and quality in the near future.

Medical image classification based on multi-scale non-negative sparse coding.

With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance.

Dynamic extreme learning machine for data stream classification

In our society, many fields have produced a large number of data streams. How to mining the interesting knowledge and patterns from continuous data stream becomes a problem which we have to solve. Different from conventional classification algorithms, data stream classification algorithms have to adjust their classification models with the change of data stream because of concept drift. However, conventional classification models will keep stable once models are trained. To solve the problem, a dynamic extreme learning machine for data stream classification (DELM) is proposed. DELM utilizes online learning mechanism to train ELM as basic classifier and trains a double hidden layer structure to improve the performance of ELM. When an alert about concept drift is set, more hidden layer nodes are added into ELM to improve the generalization ability of classifier. If the value measuring concept drift reaches the upper limit or the accuracy of ELM is in a low level, the current classifier will be deleted, and the algorithm will use new data to train a new classifier so as to learn new concept. The experimental results showed DELM could improve the accuracy of classification result, and can adapt to new concept in a short time.

Hybrid supervised clustering based ensemble scheme for text classification

PurposeThe immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in information retrieval, such as document organization, text filtering and sentiment analysis. Ensemble learning has been extensively studied to construct efficient text classification schemes with higher predictive performance and generalization ability. The purpose of this paper is to provide diversity among the classification algorithms of ensemble, which is a key issue in the ensemble design.Design/methodology/approachAn ensemble scheme based on hybrid supervised clustering is presented for text classification. In the presented scheme, supervised hybrid clustering, which is based on cuckoo search algorithm and k-means, is introduced to partition the data samples of each class into clusters so that training subsets with higher diversities can be provided. Each classifier is trained on the diversified training subsets and the predictions of individual classifiers are combined by the majority voting rule. The predictive performance of the proposed classifier ensemble is compared to conventional classification algorithms (such as Naïve Bayes, logistic regression, support vector machines and C4.5 algorithm) and ensemble learning methods (such as AdaBoost, bagging and random subspace) using 11 text benchmarks.FindingsThe experimental results indicate that the presented classifier ensemble outperforms the conventional classification algorithms and ensemble learning methods for text classification.Originality/valueThe presented ensemble scheme is the first to use supervised clustering to obtain diverse ensemble for text classification

컨볼루션 신경망을 이용한 CCTV 영상 기반의 성별구분

Recently, gender classification has attracted a great deal of attention in the field of video surveillance system. It can be useful in many applications such as detecting crimes for women and business intelligence. In this paper, we proposed a method which can detect pedestrians from CCTV video and classify the gender of the detected objects. So far, many algorithms have been proposed to classify people according the their gender. This paper presents a gender classification using convolutional neural network. The detection phase is performed by AdaBoost algorithm based on Haar-like features and LBP features. Classifier and detector is trained with data-sets generated form CCTV images. The experimental results of the proposed method is male matching rate of 89.9% and the results shows 90.7% of female videos. As results of simulations, it is shown that the proposed gender classification is better than conventional classification algorithm.

Dynamic Reconfigurable Ternary Content Addressable Memory for OpenFlow-Compliant Low-Power Packet Processing

OpenFlow, the main protocol for software-defined networking (SDN) advocated by Google, requires multiple flow tables with long and variable rule lengths. For fast packet forwarding, using ternary content-addressable memory (TCAM) as implementation of lookup-table has displayed superior performance in executing the conventional packet classification algorithms. However, applying traditional TCAM designs to an OpenFlow switch leads to problems of insufficient word length, inflexible table configuration, and high-power consumptions. This paper presents a low-power dynamic reconfigurable TCAM design for OpenFlow-compliant packet processing. It can support long and variable word lengths for different flow tables, with less than 5% of area overhead. To reduce dynamic power consumption without sacrificing throughput, the proposed hybrid-pipelined structure based on NAND-NOR CAM and pipelined-TCAM, can achieve lower energy-delay products than traditional TCAM. Moreover, the proposed self-power gating technique controlled by the mask data optimizes both NAND and NOR core cells, which lowers power consumption without additional control and routing overhead. Finally, the low-power reconfigurable TCAM is implemented in TSMC 40 nm CMOS technology. The implementation results show that the energy metric of the proposed reconfigurable TCAM is only 0.177 fJ/bit/search, at a 416 MHz operating frequency. Thus, the proposed TCAM design is very suitable for the emerging high performance packet processing in future SDN applications that need long and variable word lengths.

Face recognition using class specific dictionary learning for sparse representation and collaborative representation

Recently, sparse representation based classification (SRC) and collaborative representation based classification (CRC) have been successfully used for visual recognition and have demonstrated impressive performance. Given a test sample, SRC or CRC formulates its linear representation with respect to the training samples and then computes the residual error for each class. SRC or CRC assumes that the training samples from each class contribute equally to the dictionary in the corresponding class, i.e., the dictionary consists of the training samples in that class. This, however, leads to high residual error and instability. To overcome this limitation, we propose a class specific dictionary learning algorithm. To be specific, by introducing the dual form of dictionary learning, an explicit relationship between the basis vectors and the original image features is represented, which also enhances the interpretability. SRC or CRC can be thus considered as a special case of the proposed algorithm. Blockwise coordinate descent algorithm and Lagrange multipliers are then adopted to optimize the corresponding objective function. Extensive experimental results on five benchmark face recognition datasets show that the proposed algorithm achieves superior performance compared with conventional classification algorithms.

Considerations for Design and Implementation of a RF Emitter Localization System with Array Antennas

In this paper, design and implementation issues for a network-oriented RF emitter localization system with array antenna are discussed. For hardware, the problem of array mismatch and RF/IF channel mismatch are introduced and the calibration schemes for solving those problems are also provided. For software, it is explained how to overcome the drawback of conventional MUltiple Signal Identification and Classification (MUSIC) algorithm in a point of identifying the number of received signals and problems such as Data Association Problem and Ghost Node Problem in regard to multiple emitter localization are presented with some approaches for getting around those problems. Finally, for implementation, a criterion for arranging each of sensors and a requirement for alignment of array antenna' orientation are also given.

All-in Text: Learning Document, Label, and Word Representations Jointly

Conventional multi-label classification algorithms treat the target labels of the classification task as mere symbols that are void of an inherent semantics. However, in many cases textual descriptions of these labels are available or can be easily constructed from public document sources such as Wikipedia. In this paper, we investigate an approach for embedding documents and labels into a joint space while sharing word representations between documents and labels. For finding such embeddings, we rely on the text of documents as well as descriptions for the labels. The use of such label descriptions not only lets us expect an increased performance on conventional multi-label text classification tasks, but can also be used to make predictions for labels that have not been seen during the training phase. The potential of our method is demonstrated on the multi-label classification task of assigning keywords from the Medical Subject Headings (MeSH) to publications in biomedical research, both in a conventional and in a zero-shot learning setting.

Separation and localization of multiple distributed wideband chirps using the fractional Fourier transform

In this paper, we consider the problem of localizing the multiple distributed wideband chirp sources using the fractional Fourier transform. The model in the time domain and that in the fractional Fourier domain derived by the Taylor series expansion are presented respectively. The representation of location vector in the Dechirping domain is illustrated which is only related to the central angle. A novel direction of arrival (DOA) estimation algorithm in the Dechirping domain is proposed, which is extended from the conventional multiple signal classification (MUSIC) algorithm in the time domain. Using this algorithm, except for estimating the DOA, the incidence source number can be determined as well which is allowed to exceed the sensor number in the array. To demonstrate the performance of proposed algorithm, numerical results are conducted. Compared to the previous FrFT-MUSIC algorithm based on the assumption of point source model, the proposed algorithm performs a better estimation performance, especially for large angular spread and low signal-to-noise ratio.