Support Vector Machine Tree Research Articles

Classification and identification of foreign fibers in cotton on the basis of a support vector machine

Foreign fibers in cotton have seriously affected the quality of cotton products. The classification and identification of foreign fibers in cotton is the foundation of cotton foreign fiber automated inspection. The paper takes the typical cotton foreign fibers in China’s textile enterprises as the research object, and acquires the images under simulated actual cotton processing. The various classification features are calculated and analyzed. The results show that aspect ratio, roundness, duty cycle and I 1 are the effective features for classifying various foreign fibers. The paper puts forward a classifier of cotton foreign fibers based on a support vector machine. A Decision Tree Support Vector Machine (DTSVM) can not only avoid the non-separated region, but also improve the training speed while the training sample number gradually decreases, going through the decision tree. The DTSVM is to be used to identify the sorts of common foreign fibers in cotton. The experimental data set shows that the rates of identifying different kinds of foreign fibers are greater than 92% using the proposed DTSVM.

A modified uncorrelated linear discriminant analysis model coupled with recursive feature elimination for the prediction of bioactivity

To meet the requirements of providing accurate, robust, and interpretable prediction of bioactivity, a modified uncorrelated linear discriminant analysis (M-ULDA) model was developed. In addition, a feature selection method called recursive feature elimination (RFE), originally used for support vector machine (SVM), was introduced and modified to fit the scheme of ULDA. From the evaluation of six pharmaceutical datasets, the M-UDLA coupled with RFE showed better or comparable classification accuracy with respect to other well-studied methods such as SVM and decision trees. The RFE used for ULDA has the advantage of increasing the computational speed and provides useful insights into biochemical mechanisms related to pharmaceutical activity by significantly reducing the number of variables used for the final model.

Research of multi-class algorithm based on fuzzy support vector machine

An approach of binary tree fuzzy multi-class support vector machines algorithm was proposed due to the multi-classification problem and sensitivity to the noisy data of the Support Vector Machine algorithm(SVM).The algorithm introduced a K-Nearest Neighbor(KNN) fuzzy membership function according to Binary Tree Support Vector Machine algorithm(BTSVM).Depending on the different influences of respective data set on the classification results and measuring method of the KNN fuzzy membership function,it can calculate the corresponding value and additionally obtain the different penalty value and ignore the unimportant data for the classification results,while constructing the classification hyper-planes.Experimental results indicate that the algorithm has a better ability of anti-interference and better classification effects.

SVM을 이용한 웨이블릿 기반 프로파일 분류에 관한 연구

베어링은 각종 설비에서 활용되는 중요한 기계요소 중 하나이다. 설비고장의 상당수는 베어링의 결함이나 파손에 기인하고 있다. 따라서 베어링에 대한 온라인모니터링기술은 설비의 정지를 예방하고 손실을 줄이는 데 필수적이다. 본 논문은 진동 신호를 이용하여 베어링의 상태를 예측하기 위한 온라인모니터링에 대해 연구한다. 프로파일로 주어지는 진동신호는 이산 웨이블릿 변환을 통해 분석되고, 분해수준별 웨이블릿 계수로부터 얻은 통계적 특징 중 유의한 것을 선별하고자 분산분석 (ANOVA)을 이용한다. 선별된 특징벡터는 Support Vector Machine (SVM)의 입력이 되는 데, 본 논문에서는 다중클래스 분류문제를 다루기 위한 계층적 SVM 트리를 제안한다. 수치실험 결과, 제안된 방법은 베어링의 결함을 분류하는 데 우수한 성능을 갖는 것으로 나타났다. Bearing is one of the important mechanical elements used in various industrial equipments. Most of failures occurred during the equipment operation result from bearing defects and breakages. Therefore, monitoring of bearings is essential in preventing equipment breakdowns and reducing unexpected loss. The purpose of this paper is to present an online monitoring method to predict bearing states using vibration signals. Bearing vibrations, which are collected as a form of profile signal, are first analyzed by a discrete wavelet transform. Next, some statistical features are obtained from the resultant wavelet coefficients. In order to select significant ones among them, analysis of variance (ANOVA) is employed in this paper. Statistical features screened in this way are used as input variables to support vector machine (SVM). An hierarchical SVM tree is proposed for dealing with multi-class problems. The result of numerical experiments shows that the proposed SVM tree has a competent performance for classifying bearing fault states.

Optimal feature selection using genetic algorithm for mechanical fault detection of induction motor

Time-domain vibration signals are measured in all horizontal, axial, and vertical directions for induction motor mechanical fault diagnostics. Many features are extracted from these signals. The problem is how to find the good features among the feature set in order to receive reliable classifications. Based on specific distance criteria, a genetic algorithm (GA) is introduced to reduce the number of features by selecting optimized ones for fault classification purpose. A decision tree and multi-class support vector machine are used to illustrate the potentiality and efficiency of this selection method. Comparisons show that the diagnostic systems after selecting specific features perform better than the original system.

Binary tree of SVM: a new fast multiclass training and classification algorithm

We present a new architecture named Binary Tree of support vector machine (SVM), or BTS, in order to achieve high classification efficiency for multiclass problems. BTS and its enhanced version, c-BTS, decrease the number of binary classifiers to the greatest extent without increasing the complexity of the original problem. In the training phase, BTS has N - 1 binary classifiers in the best situation (N is the number of classes), while it has log4/3 ((N + 3)/4) binary tests on average when making a decision. At the same time the upper bound of convergence complexity is determined. The experiments in this paper indicate that maintaining comparable accuracy, BTS is much faster to be trained than other methods. Especially in classification, due to its Log complexity, it is much faster than directed acyclic graph SVM (DAGSVM) and ECOC in problems that have big class number.

Automatic CRP mapping using nonparametric machine learning approaches

This paper studies an uneven two-class unsupervised classification problem of satellite imagery, i.e., the mapping of U.S. Department of Agriculture's (USDA) Conservation Reserve Program (CRP) tracts. CRP is a nationwide program that encourages farmers to plant long-term, resource conserving covers to improve soil, water, and wildlife resources. With recent payments of nearly US $1.6 billion for new enrollments (2002 signup), it is imperative to obtain accurate digital CRP maps for management and evaluation purposes. CRP mapping is a complex classification problem where both CRP and non-CRP areas are composed of various cover types. Two nonparametric machine learning approaches, i.e., decision tree classifier (DTC) and support vector machine (SVMs) are implemented in this work. Specifically, considering the importance of CRP classification sensitivity, a new DTC pruning method is proposed to increase recall. We also study two SVM relaxation approaches to increase recall. Moreover, a localized and parallel framework is suggested in order to efficiently deal with the large-scale CRP mapping need. Simulation results validate the applicability of the suggested framework and proposed techniques.

Face Membership Authentication Using SVM Classification Tree Generated by Membership-Based LLE Data Partition

This paper presents a new membership authentication method by face classification using a support vector machine (SVM) classification tree, in which the size of membership group and the members in the membership group can be changed dynamically. Unlike our previous SVM ensemble-based method, which performed only one face classification in the whole feature space, the proposed method employed a divide and conquer strategy that first performs a recursive data partition by membership-based locally linear embedding (LLE) data clustering, then does the SVM classification in each partitioned feature subset. Our experimental results show that the proposed SVM tree not only keeps the good properties that the SVM ensemble method has, such as a good authentication accuracy and the robustness to the change of members, but also has a considerable improvement on the stability under the change of membership group size.

Sentiment Classification of Product Reviews Using SVM and Decision Tree Induction

This paper reports a study in automatic sentiment classification, i.e. automatically classifying documents as expressing positive or negative sentiments/opinions. The study investigates the effectiveness of using SVM (Support Vector Machine) and Decision Tree induction on various text features to classify product reviews into recommended (positive sentiment) and not recommended (negative sentiment). Compared with traditional topical classification, it was hypothesized that syntactic and semantic processing of text would be more important for sentiment classification. In this study, five different approaches, unigrams (individual words), part-of-speech tagging, association rules, use of negation, and use of WordNet synsets (identifying a set of synonyms) were investigated. A sample of 1,800 miscellaneous product reviews was retrieved from Review Centre (www.reviewcentre.com. 2003) for the study. 1,200 reviews were used for training, and 600 for validation. Using SVM, the baseline unigrams approach obtained an accuracy rate of 81.3%. The use of WordNet synsets obtained marginally better result of 81.7%. The other text features did not yield better results. Error analysis suggests 3 approaches for improving classification accuracy: making inference from superficial words, solving the problems of comments on parts and negation. Finally, Decision Tree induction was used to generate a list of indicative words that can identify the polarity of articles.

Geno2pheno: interpreting genotypic HIV drug resistance tests

This intelligent system uses information encoded in the HIV genomic sequence to predict the virus's resistance or susceptibility to drugs. To make predictions, geno2pheno employs decision tree classifiers and support vector machines.