• Title/Summary/Keyword: classifier

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Text-independent Speaker Identification by Bagging VQ Classifier

  • Kyung, Youn-Jeong;Park, Bong-Dae;Lee, Hwang-Soo
    • The Journal of the Acoustical Society of Korea
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    • v.20 no.2E
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    • pp.17-24
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    • 2001
  • In this paper, we propose the bootstrap and aggregating (bagging) vector quantization (VQ) classifier to improve the performance of the text-independent speaker recognition system. This method generates multiple training data sets by resampling the original training data set, constructs the corresponding VQ classifiers, and then integrates the multiple VQ classifiers into a single classifier by voting. The bagging method has been proven to greatly improve the performance of unstable classifiers. Through two different experiments, this paper shows that the VQ classifier is unstable. In one of these experiments, the bias and variance of a VQ classifier are computed with a waveform database. The variance of the VQ classifier is compared with that of the classification and regression tree (CART) classifier[1]. The variance of the VQ classifier is shown to be as large as that of the CART classifier. The other experiment involves speaker recognition. The speaker recognition rates vary significantly by the minor changes in the training data set. The speaker recognition experiments involving a closed set, text-independent and speaker identification are performed with the TIMIT database to compare the performance of the bagging VQ classifier with that of the conventional VQ classifier. The bagging VQ classifier yields improved performance over the conventional VQ classifier. It also outperforms the conventional VQ classifier in small training data set problems.

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Prediction of Academic Performance of College Students with Bipolar Disorder using different Deep learning and Machine learning algorithms

  • Peerbasha, S.;Surputheen, M. Mohamed
    • International Journal of Computer Science & Network Security
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    • v.21 no.7
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    • pp.350-358
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    • 2021
  • In modern years, the performance of the students is analysed with lot of difficulties, which is a very important problem in all the academic institutions. The main idea of this paper is to analyze and evaluate the academic performance of the college students with bipolar disorder by applying data mining classification algorithms using Jupiter Notebook, python tool. This tool has been generally used as a decision-making tool in terms of academic performance of the students. The various classifiers could be logistic regression, random forest classifier gini, random forest classifier entropy, decision tree classifier, K-Neighbours classifier, Ada Boost classifier, Extra Tree Classifier, GaussianNB, BernoulliNB are used. The results of such classification model deals with 13 measures like Accuracy, Precision, Recall, F1 Measure, Sensitivity, Specificity, R Squared, Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, TPR, TNR, FPR and FNR. Therefore, conclusion could be reached that the Decision Tree Classifier is better than that of different algorithms.

A New Approach to the Design of Combining Classifier Based on Immune Algorithm

  • Kim, Moon-Hwan;Jeong, Keun-Ho;Joo, Young-Hoon;Park, Jin-Bae
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1272-1277
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    • 2003
  • This paper presents a method for combining classifier which is constructed by fuzzy and neural network classifiers and uses classifier fusion algorithms and selection algorithms. The input space of combing classifier is divided by the extended hyperbox region proposed in this paper to guarantee non-overlapped data property. To fuse the fuzzy classifier and the neural network classifier, we propose the fusion parameter for the overlapped data. In addition, the adaptive learning algorithm also proposed to maximize classifier performance. Finally, simulation examples are given to illustrate the effectiveness of the method.

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A Method to Improve the Performance of Adaboost Algorithm by Using Mixed Weak Classifier (혼합 약한 분류기를 이용한 AdaBoost 알고리즘의 성능 개선 방법)

  • Kim, Jeong-Hyun;Teng, Zhu;Kim, Jin-Young;Kang, Dong-Joong
    • Journal of Institute of Control, Robotics and Systems
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    • v.15 no.5
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    • pp.457-464
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    • 2009
  • The weak classifier of AdaBoost algorithm is a central classification element that uses a single criterion separating positive and negative learning candidates. Finding the best criterion to separate two feature distributions influences learning capacity of the algorithm. A common way to classify the distributions is to use the mean value of the features. However, positive and negative distributions of Haar-like feature as an image descriptor are hard to classify by a single threshold. The poor classification ability of the single threshold also increases the number of boosting operations, and finally results in a poor classifier. This paper proposes a weak classifier that uses multiple criterions by adding a probabilistic criterion of the positive candidate distribution with the conventional mean classifier: the positive distribution has low variation and the values are closer to the mean while the negative distribution has large variation and values are widely spread. The difference in the variance for the positive and negative distributions is used as an additional criterion. In the learning procedure, we use a new classifier that provides a better classifier between them by selective switching between the mean and standard deviation. We call this new type of combined classifier the "Mixed Weak Classifier". The proposed weak classifier is more robust than the mean classifier alone and decreases the number of boosting operations to be converged.

Misclassified Samples based Hierarchical Cascaded Classifier for Video Face Recognition

  • Fan, Zheyi;Weng, Shuqin;Zeng, Yajun;Jiang, Jiao;Pang, Fengqian;Liu, Zhiwen
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.2
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    • pp.785-804
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    • 2017
  • Due to various factors such as postures, facial expressions and illuminations, face recognition by videos often suffer from poor recognition accuracy and generalization ability, since the within-class scatter might even be higher than the between-class one. Herein we address this problem by proposing a hierarchical cascaded classifier for video face recognition, which is a multi-layer algorithm and accounts for the misclassified samples plus their similar samples. Specifically, it can be decomposed into single classifier construction and multi-layer classifier design stages. In single classifier construction stage, classifier is created by clustering and the number of classes is computed by analyzing distance tree. In multi-layer classifier design stage, the next layer is created for the misclassified samples and similar ones, then cascaded to a hierarchical classifier. The experiments on the database collected by ourselves show that the recognition accuracy of the proposed classifier outperforms the compared recognition algorithms, such as neural network and sparse representation.

Layered Classifier System by Classification of Environment

  • Kim, Ji-Yoon;Lee, Dong-Wook;Sim, Kwee-Bo
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1517-1520
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    • 2003
  • Generally, the environment we want to apply classifier system to is composed of several state spaces. So in this paper, we propose the layered classifier system having multifarious rule bases. From sensor's inputs, the lower layer of the layered classifier system learns strategies for each environmental state space. The higher layer learns how to allot each rule base of the strategy for environmental state space properly. To evaluate the proposed architecture of classifier system, we designed virtual environment having multifarious state spaces and from the analysis of the experimental results, we affirm that layered classifier system could find better strategies during a little time than other established classifier system's findings.

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Design of the Pattern Classifier using Fuzzy Neural Network (퍼지 신경 회로망을 이용한 패턴 분류기의 설계)

  • Kim, Moon-Hwan;Lee, Ho-Jae;Joo, Young-Hoon;Park, Jin-Bae
    • Proceedings of the KIEE Conference
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    • pp.2573-2575
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    • 2003
  • In this paper, we discuss a fuzzy neural network classifier with immune algorithm. The fuzzy neural network classifier is constructed with the fuzzy classifier and the neural network classifier based on fuzzy rules. To maximize performance of classifier, the immune algorithm and the back propagation algorithm are used. For the generalized classification ability, the simulation results from the iris data demonstrate superiority of the proposed classifier in comparison with other classifier.

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An experimental study for the coal particle separator in the pulverizer model with dynamic classifier (Dynamic classifier가 장착된 미분기 모델에서의 석탄 입자 분리 실험)

  • Lee, Gun-Myung;Kim, Hyuk-Je;Kim, Hyeuk-Pill;Kim, Sang-Hyeun;Ha, Jong-Kang
    • Proceedings of the KSME Conference
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    • pp.688-692
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    • 2001
  • Three-dimensional experimental analyses were conducted in the pulverizer simplified isothermal model. The experimental model was constructed on a 1/3.5 scale of 500MW pulverized coal boiler. The purpose of this study is to investigate the characteristics of coal particle separator and the pressure loss in the pulverizer models with dynamic classifier. Without regards a shape of separator top, the results showed that the increase of dynamic classifier rpm was induced in finer coal particle. But the capacity of total mass per minute was reduced. Also, the increase of dynamic classifier rpm had no effect on total pressure loss, but an increase of inlet velocity was induced that the rise of total pressure loss in the pulverizer models with dynamic classifier.

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Design of Precipitation/non-precipitation Pattern Classification System based on Neuro-fuzzy Algorithm using Meteorological Radar Data : Instance Classifier and Echo Classifier (기상레이더를 이용한 뉴로-퍼지 알고리즘 기반 강수/비강수 패턴분류 시스템 설계 : 사례 분류기 및 에코 분류기)

  • Ko, Jun-Hyun;Kim, Hyun-Ki;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.64 no.7
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    • pp.1114-1124
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    • 2015
  • In this paper, precipitation / non-precipitation pattern classification of meteorological radar data is conducted by using neuro-fuzzy algorithm. Structure expression of meteorological radar data information is analyzed in order to effectively classify precipitation and non-precipitation. Also diverse input variables for designing pattern classifier could be considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data information and then each characteristic of input variables is analyzed. Preferred pattern classifier can be designed by essential input variables that give a decisive effect on output performance as well as model architecture. As the proposed model architecture, neuro-fuzzy algorithm is designed by using FCM-based radial basis function neural network(RBFNN). Two parts of classifiers such as instance classifier part and echo classifier part are designed and carried out serially in the entire system architecture. In the instance classifier part, the pattern classifier identifies between precipitation and non-precipitation data. In the echo classifier part, because precipitation data information identified by the instance classifier could partially involve non-precipitation data information, echo classifier is considered to classify between them. The performance of the proposed classifier is evaluated and analyzed when compared with existing QC method.

Pruning and Learning Fuzzy Rule-Based Classifier

  • Kim, Do-Wan;Park, Jin-Bae;Joo, Young-Hoon
    • 제어로봇시스템학회:학술대회논문집
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    • pp.663-667
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    • 2004
  • This paper presents new pruning and learning methods for the fuzzy rule-based classifier. The structure of the proposed classifier is framed from the fuzzy sets in the premise part of the rule and the Bayesian classifier in the consequent part. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the decision region to one for all feature values. For the improvement of the classification performance, the parameters of the proposed classifier are finely adjusted by using the gradient descent method so that the misclassified feature vectors are correctly re-categorized. The cost function is determined as the squared-error between the classifier output for the correct class and the sum of the maximum output for the rest and a positive scalar. Then, the learning rules are derived from forming the gradient. Finally, the fuzzy rule-based classifier is tested on two data sets and is found to demonstrate an excellent performance.

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