Centroid-based Classifier Research Articles

A Hand Gesture Recognition Circuit Utilizing an Analog Voting Classifier

Electromyography is a diagnostic medical procedure used to assess the state of a muscle and its related nerves. Electromyography signals are monitored to detect neuromuscular abnormalities and diseases but can also prove useful in decoding movement-related signals. This information is vital to controlling prosthetics in a more natural way. To this end, a novel analog integrated voting classifier is proposed as a hand gesture recognition system. The voting classifiers utilize 3 separate centroid-based classifiers, each one attached to a different electromyographic electrode and a voting circuit. The main building blocks of the architecture are bump and winner-take-all circuits. To confirm the proper operation of the proposed classifier, its post-layout classification results (91.2% accuracy) are compared to a software-based implementation (93.8% accuracy) of the same voting classifier. A TSMC 90 nm CMOS process in the Cadence IC Suite was used to design and simulate the following circuits and architectures.

CLASSIFICATION OF MEDICAL IMAGES USING MACHINE LEARNING

The popularity of the use of computational tools such as artificial intelligence has been increasing in recent years, and its importance in medicine is a fact. This field has benefited greatly thanks to the incorporation of more effective and faster methodologies in the medical diagnosis and registration processes. In the present work, the classification of images related to three diseases: Tuberculosis, Glaucoma and Parkinson's is carried out. We used deep learning and the RESNET50 convolutional neural network to extract classification characteristics, and then perform the classification based on standard methods, such as support vector machines, Naïve Bayes, and Centroid-based classifier, which are incorporated into two scenarios (cross validation; training and test sets). The classifier's performance is evaluated quantitatively using three evaluation metrics. The results obtained support the feasibility of the proposed methodology and its potential to improve medical diagnosis.

A Novel Multi-Centroid Template Matching Algorithm and Its Application to Cough Detection.

Cough is a major symptom of respiratory-related diseases. There exists a tremendous amount of work in detecting coughs from audio but there has been no effort to identify coughs from solely inertial measurement unit (IMU). Coughing causes motion across the whole body and especially on the neck and head. Therefore, head motion data during coughing captured by a head-worn IMU sensor could be leveraged to detect coughs using a template matching algorithm. In time series template matching problems, K-Nearest Neighbors (KNN) combined with elastic distance measurement (esp. Dynamic Time Warping (DTW)) achieves outstanding performance. However, it is often regarded as prohibitively time-consuming. Nearest Centroid Classifier is thereafter proposed. But the accuracy is comprised of only one centroid obtained for each class. Centroid-based Classifier performs clustering and averaging for each cluster, but requires manually setting the number of clusters. We propose a novel self-tuning multi-centroid template-matching algorithm, which can automatically adjust the number of clusters to balance accuracy and inference time. Through experiments conducted on synthetic datasets and a real-world earbud-based cough dataset, we demonstrate the superiority of our proposed algorithm and present the result of cough detection with a single accelerometer sensor on the earbuds platform.Clinical relevance- Coughing is a ubiquitous symptom of pulmonary disease, especially for patients with COPD and asthma. This work explores the possibility and and presents the result of cough detection using an IMU sensor embedded in earables.

Effect of Term Weighting on Keyword Extraction in Hierarchical Category Structure

While there have been several studies related to the effect of term weighting on classification accuracy, relatively few works have been conducted on how term weighting affects the quality of keywords extracted for characterizing a document or a category (i.e., document collection). Moreover, many tasks require more complicated category structure, such as hierarchical and network category structure, rather than a flat category structure. This paper presents a qualitative and quantitative study on how term weighting affects keyword extraction in the hierarchical category structure, in comparison to the flat category structure. A hierarchical structure triggers special characteristic in assigning a set of keywords or tags to represent a document or a document collection, with support of statistics in a hierarchy, including category itself, its parent category, its child categories, and sibling categories. An enhancement of term weighting is proposed particularly in the form of a series of modified TFIDF's, for improving keyword extraction. A text collection of public-hearing opinions is used to evaluate variant TFs and IDFs to identify which types of information in hierarchical category structure are useful. By experiments, we found that the most effective IDF family, namely TF-IDFr, is identity>sibling>child>parent in order. The TF-IDFr outperforms the vanilla version of TFIDF with a centroid-based classifier.

BRCAness digitalMLPA profiling predicts benefit of intensified platinum-based chemotherapy in triple-negative and luminal-type breast cancer

BackgroundWe previously showed that BRCA-like profiles can be used to preselect individuals with the highest risk of carrying BRCA mutations but could also indicate which patients would benefit from double-strand break inducing chemotherapy. A simple, robust, and reliable assay for clinical use that utilizes limited amounts of formalin-fixed, paraffin-embedded tumor tissue to assess BRCAness status in both ER-positive and ER-negative breast cancer (BC) is currently lacking.MethodsA digital multiplex ligation-dependent probe amplification (digitalMLPA) assay was designed to detect copy number alterations required for the classification of BRCA1-like and BRCA2-like BC. The BRCA1-like classifier was trained on 71 tumors, enriched for triple-negative BC; the BRCA2-like classifier was trained on 55 tumors, enriched for luminal-type BC. A shrunken centroid-based classifier was developed and applied on an independent validation cohort. A total of 114 cases of a randomized controlled trial were analyzed, and the association of the classifier result with intensified platinum-based chemotherapy response was assessed.ResultsThe digitalMLPA BRCA1-like classifier correctly classified 91% of the BRCA1-like samples and 82% of the BRCA2-like samples. Patients with a BRCA-like tumor derived significant benefit of high-dose chemotherapy (adjusted hazard ratio (HR) 0.12, 95% CI 0.04–0.44) which was not observed in non-BRCA-like patients (HR 0.9, 95% CI 0.37–2.18) (p = 0.01). Analysis stratified for ER status showed borderline significance.ConclusionsThe digitalMLPA is a reliable method to detect a BRCA1- and BRCA2-like pattern on clinical samples and predicts platinum-based chemotherapy benefit in both triple-negative and luminal-type BC.

A Methodology Combining Cosine Similarity with Classifier for Text Classification

ABSTRACTText Classification has received significant attention in recent years because of the proliferation of digital documents and is widely used in various applications such as filtering and recommendation. Consequently, many approaches, including those based on statistical theory, machine learning, and classifier performance improvement, have been proposed for improving text classification performance. Among these approaches, centroid-based classifier, multinomial naïve bayesian (MNB), support vector machines (SVM), convolutional neural network (CNN) are commonly used. In this paper, we introduce a cosine similarity-based methodology for improving performance. The methodology combines cosine similarity (between a test document and fixed categories) with conventional classifiers such as MNB, SVM, and CNN to improve the accuracy of the classifiers, and then we call the conventional classifiers with cosine similarity as enhanced classifiers. We applied the enhanced classifiers to famous datasets – 20NG, R8, R52, Cade12, and WebKB – and evaluated the performance of the enhanced classifiers in terms of the confusion matrix’s accuracy; we obtained outstanding results in that the enhanced classifiers show significant increases in accuracy. Moreover, through experiments, we identified which of two considered knowledge representation techniques (word count and term frequency-inverse document frequency (TFIDF)) is more suitable in terms of classifier performance.

Term evaluation metrics in imbalanced text categorization

AbstractThis paper proposes four novel term evaluation metrics to represent documents in the text categorization where class distribution is imbalanced. These metrics are achieved from the revision of the four common term evaluation metrics:chi-square,information gain,odds ratio, andrelevance frequency. While the common metrics require a balanced class distribution, our proposed metrics evaluate the document terms under an imbalanced distribution. They calculate the degree of relatedness of terms with respect to minor and major classes by considering their imbalanced distribution. Using these metrics in the document representation makes a better distinction between the documents of the minor and major classes and improves the performance of machine learning algorithms. The proposed metrics are assessed over three popular benchmarks (two subsets of Reuters-21578 and WebKB) by using four classification algorithms: support vector machines, naive Bayes, decision trees, and centroid-based classifiers. Our empirical results indicate that the proposed metrics outperform the common metrics in the imbalanced text categorization.

A new Centroid-Based Classification model for text categorization

The automatic text categorization technique has gained significant attention among researchers because of the increasing availability of online text information. Therefore, many different learning approaches have been designed in the text categorization field. Among them, the widely used method is the Centroid-Based Classifier (CBC) due to its theoretical simplicity and computational efficiency. However, the classification accuracy of CBC greatly depends on the data distribution. Thus it leads to a misfit model and also has poor classification performance when the data distribution is highly skewed. In this paper, a new classification model named as Gravitation Model (GM) is proposed to solve the class-imbalanced classification problem. In the training phase, each class is weighted by a mass factor, which can be learned from the training data, to indicate data distribution of the corresponding class. In the testing phase, a new document will be assigned to a particular class with the max gravitational force. The performance comparisons with CBC and its variants based on the results of experiments conducted on twelve real datasets show that the proposed gravitation model consistently outperforms CBC together with the Class-Feature-Centroid Classifier (CFC). Also, it obtains the classification accuracy competitive to the DragPushing (DP) method while it maintains a more stable performance. Thus, the proposed gravitation model is proved to be less over-fitting and has higher learning ability than CBC model.

A Weighted Method to Improve the Centroid-based Classifier

Centroid-Based Classifier (CBC) is one of the most widely used text classification method due to its theoretical simplicity and computational efficiency. However, the accuracy of CBC is not satisfactory when it deals with the skewed distributed data. In this paper, we propose a new classification model named as Gravitation Model (GM) to solve the model misfit of CBC. In the proposed model, we give each category a mass factor to indicate its distribution in vector space and this factor can be learned from training data. We provide the performance comparisons with CBC and its improved methods based on the results of experiments conducted on twelve real datasets, which show that the proposed gravitation model consistently outperforms CBC. Furthermore, it reaches the same performance as the best centroid-based classifier and is more stable than the best one.

A new feature selection method based on a validity index of feature subset

The wrapper feature selection method can achieve high classification accuracy. However, the cross-validation scheme of the wrapper method in evaluation phase is very expensive regarding computing resource consumption. In this paper, we propose a new statistical measure named as LW-index which could replace the expensive cross-validation scheme to evaluate the feature subset. Then, a new feature selection method, which is the combination of the proposed LW-index with Sequence Forward Search algorithm (SFS-LW), is presented in this paper. Further, we show through plenty of experiments conducted on nine UCI datasets that the proposed method can obtain similar classification accuracy as the wrapper method with centroid-based classifier or support vector machine, and its computation cost is approximate to the compared filter methods.

A new validity index of feature subset for evaluating the dimensionality reduction algorithms

A critical aspect of dimensionality reduction is to assess the quality of selected (or produced) feature subsets properly. Feature subset assessment in machine learning refers to split a given feature subset into a training set, which is used to estimate the parameters of a classification model, and a test set used to estimate the predictive performance of the model. Then, averaging the results of multiple splitting (i.e., Cross-Validation, CV) is commonly used to decrease the variance of the estimator. But in practice, CV scheme is very computationally expensive. In this paper, we propose a new statistics index method called LW-index for evaluation of feature subset and dimensionality reduction algorithms in general. The proposed method is a type of “classical statistics” approach that uses the feature subset to compute an empirical estimate of the quality of feature subset. A large number of performance comparisons with the machine learning approach conducted on fourteen benchmark collections show that the proposed LW-index is highly correlated with the external indices (i.e., MacroF1, MicroF1) of SVM and Centroid-Based Classifier (CBC) trained by five-fold CV scheme. Furthermore, the experimental results indicate that LW-index has the same performance as the traditional CV scheme for evaluating the dimensionality reduction algorithms and it is more efficient than the traditional methodology. Therefore, one contribution of this paper is to present an alternative methodology, based on an internal index typically used in the unsupervised learning context, that is computationally cheaper than the traditional CV methodology. Another contribution is to propose a new internal index that behaves better than other similar indices widely used in clustering and shows high correlation with the results obtained by the traditional methodology.

Multi-Label Genre Classification of Web Pages Using an Adaptive Centroid-Based Classifier

This paper proposes an adaptive centroid-based classifier (ACC) for multi-label classification of web pages. Using a set of multi-genre training dataset, ACC constructs a centroid for each genre. To deal with the rapid evolution of web genres, ACC implements an adaptive classification method where web pages are classified one by one. For each web page, ACC calculated its similarity with all genre centroids. Based on this similarity, ACC either adjusts the genre centroid by including the new web page or discards it. A web page is a complex object that contains different sections belonging to different genres. To handle this complexity, ACC implements a multi-label classification where a web page can be assigned to multiple genres at the same time. To improve the performance of genre classification, we propose to aggregate the classifications produced using character n-grams extracted from URL, title, headings and anchors. Experiments conducted using a known multi-label dataset show that ACC outperforms many other multi-label classifiers and has the lowest computational complexity.

A kernel-based centroid classifier using hypothesis margin

The centroid-based classifier is both effective and efficient for document classification. However, it suffers from over-fitting and linear inseparability problems caused by its fundamental assumptions. To address these problems, we propose a kernel-based hypothesis margin centroid classifier (KHCC). First, KHCC optimises the class centroids via minimising hypothesis margin under structural risk minimisation principle; second, KHCC uses the kernel method to relieve the problem of linear inseparability in the original feature space. Given the radial basis function, we further discuss a guideline for tuning the value of its parameter. The experimental results on four well-known data-sets indicate that our KHCC algorithm outperforms the state-of-the-art algorithms, especially for the unbalanced data-set.

Categorising texts more accurately with field association terms

Popular text classification algorithms such as Naive Bayes, kNN, Centroid-based classifiers and support vector machines SVM are based on supervised machine learning. They normally use classical text representation technique consisting of a 'bag of words' as features. This representation leads to the inclusion of unimportant features, and the loss of important semantic relationships and inflection information, resulting in accuracy reduction. To address this problem, we propose a new text classification methodology based on field association terms - a set of terms that identify specific document fields. The methodology is compared against Naive Bayes, kNN, Centroid-based classifier and SVM on a close dataset of 3180 documents from Wikipedia dumps and open dataset of 9449 documents from Reuters RCV1 Corpus, 20-Newsgroup and 4-Universities datasets. The new method outperformed the other algorithms with a precision of 97% as compared with Centroid-based 85%, Naive Bayes 78%, kNN 48% and SVM 42%.

Enhanced and combined centroid-based approach for multi-label genre classification of web pages

This paper proposes an enhanced and combined centroid-based approach to classify web pages by genre. To deal with the complexity of web pages, the proposed approach implements a multi-label classification scheme in which a web page can be affected to more than one genre. In addition, it implements an incremental classification to handle the rapid evolution of web genres. In this classification, web pages are classified one by one, according to the similarity between the new page and each genre centroid, our approach either adjusts the genre centroid or considers the new page as noise page and discards it. Moreover, our approach combines three homogenous and centroid-based classifiers: contextual, logical and hyper link classifiers. These classifiers exploit the character n-grams extracted from different sources which are URL, title, headings and anchors. Experiments conducted using a known multi-label corpus showing that our approach is very fast and outperforms many other multi-label classifiers.

Chinese Text Categorization via Bottom-Up Weighted Word Clustering

Most of the researches on text categorization are focus on using bag of words. Some researches provided other methods for classification such as term phrase, Latent Semantic Indexing, and term clustering. Term clustering is an effective way for classification, and had been proved as a good method for decreasing the dimensions in term vectors. The authors used hierarchical term clustering and aggregating similar terms. In order to enhance the performance, they present a modify indexing with terms in cluster. Their test collection extracted from Chinese NETNEWS, and used the Centroid-Based classifier to deal with the problems of categorization. The results had shown that term clustering is not only reducing the dimensions but also outperform than bag of words. Thus, term clustering can be applied to text classification by using any large corpus, its objective is to save times and increase the efficiency and effectiveness. In addition to performance, these clusters can be considered as conceptual knowledge base, and kept related terms of real world.

Using Different Term Weighting Schemes of Centroid-Based Classifiers to Classify Drug Monographs

With an increasing number of documents for drug monographs on the Internet, automatic classification of documents is an important task for organizing these documents into appropriated classes. The monographs of drug can be regularly categorized by their indications. A centroid-based classifier is a relatively high performance classifier with relatively less computation. To enhance the efficiency of standard centroid-based classifier with TFIDF to classify drug monographs, different term weighting schemes of a centroid-based classifier are evaluated. Moreover, the combination of a set of centroid-based classifiers with different term weighting schemes is proposed in this work. To evaluate the proposed method, two set of drug monographs are drawn from DailyMed and RxList websites are used. From the experimental results, the proposed method can improve the performance of the centroid-based classifier.

A Document Level Measure for Text Categorization

The term weighting scheme in text categorization is a vital step in automatic text categorization. Previous studies showed that term weighting techniques contribute more to the accuracy of classification than that of the classifier’s contribution for the same. So this work is concentrated on term weighting schemes for text categorization. A new supervised term weighting scheme for text categorization is proposed. The frequency of each term in a document is expressed as probability of the terms in a document. This gives the proportion of each term in a document. This information provides with a very good knowledge on the category of the document. The probability of a term in all the documents of a class when summed up leads to a very important variable which can be used for term weighting in classification. This is basically a document level variable because it is related to the probability of a term in a document. The related new measure is named as td (terms in a document). Its performance when evaluated with reuters-21578 and 20Newsgroup dataset showed interesting increase in performance compared to tf, idf and rf. Compared to rf, this measure works well for both svm (binary classifier) and centroid-based classifiers(multiclass classifier).

Inverse-Category-Frequency Based Supervised Term Weighting Schemes for Text Categorization *

Term weighting schemes often dominate the performance of many classifiers, such as kNN, centroid-based classifier and SVMs. The widely used term weighting scheme in text categorization, i.e., tf.idf, is originated from information retrieval (IR) field. The intuition behind idf for text categorization seems less reasonable than IR. In this paper, we introduce inverse category frequency (icf) into term weighting scheme and propose two novel approaches, i.e., tf.icf and icf-based supervised term weighting schemes. The tf.icf adopts icf to substitute idf factor and favors terms occurring in fewer categories, rather than fewer documents. And the icf-based approach combines icf and relevance frequency (rf) to weight terms in a supervised way. Our cross-classifier and cross-corpus experiments have shown that our proposed approaches are superior or comparable to six supervised term weighting schemes and three traditional schemes in terms of macro-F1 and micro-F1.

Towards enhancing centroid classifier for text classification—A border-instance approach

Text classification/categorization (TC) is to assign new unlabeled natural language documents to the predefined thematic categories. Centroid-based classifier (CC) has been widely used for TC because of its simplicity and efficiency. However, it has also been long criticized for its relatively low classification accuracy compared with state-of-the-art classifiers such as support vector machines (SVMs). In this paper, we find that for CC using only border instances rather than all instances to construct centroid vectors can obtain higher generalization accuracy. Along this line, we propose Border-Instance-based Iteratively Adjusted Centroid Classifier (IACC_BI), which relies on the border instances found by some routines, e.g. 1-Nearest-and-1-Furthest-Neighbors strategy, to construct centroid vectors for CC. IACC_BI then iteratively adjusts the initial centroid vectors according to the misclassified training instances. Our extensive experiments on 11 real-world text corpora demonstrate that IACC_BI improves the performance of centroid-based classifiers greatly and obtains classification accuracy competitive to the well-known SVMs, while at significantly lower computational costs.