Kernel-Free Quadratic Surface Minimax Probability Machine for a Binary Classification Problem

In this paper, we propose a novel binary classification method called the kernel-free quadratic surface minimax probability machine (QSMPM), that makes use of the kernel-free techniques of the quadratic surface support vector machine (QSSVM) and inherits the advantage of the minimax probability machine (MPM) without any parameters. Specifically, it attempts to find a quadratic hypersurface that separates two classes of samples with maximum probability. However, the optimization problem derived directly was too difficult to solve. Therefore, a nonlinear transformation was introduced to change the quadratic function involved into a linear function. Through such processing, our optimization problem finally became a second-order cone programming problem, which was solved efficiently by an alternate iteration method. It should be pointed out that our method is both kernel-free and parameter-free, making it easy to use. In addition, the quadratic hypersurface obtained by our method was allowed to be any general form of quadratic hypersurface. It has better interpretability than the methods with the kernel function. Finally, in order to demonstrate the geometric interpretation of our QSMPM, five artificial datasets were implemented, including showing the ability to obtain a linear separating hyperplane. Furthermore, numerical experiments on benchmark datasets confirmed that the proposed method had better accuracy and less CPU time than corresponding methods.

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Soft Quadratic Surface Support Vector Machine for Binary Classification

Asia Pacific Journal of Operational Research ◽

10.1142/s0217595916500469 ◽

2016 ◽

Vol 33 (06) ◽

pp. 1650046 ◽

Cited By ~ 6

Author(s):

Jian Luo ◽

Shu-Cherng Fang ◽

Zhibin Deng ◽

Xiaoling Guo

Keyword(s):

Support Vector Machine ◽

Support Vector Machine Model ◽

Binary Classification ◽

Optimal Solution ◽

Quadratic Function ◽

Gaussian Kernel ◽

Quadratic Model ◽

Support Vector ◽

Machine Model ◽

Quadratic Surface

In this paper, a kernel-free soft quadratic surface support vector machine model is proposed for binary classification directly using a quadratic function for separation. Properties (including the solvability, uniqueness and support vector representation of the optimal solution) of the proposed model are derived. Results of computational experiments on some artificial and real-world classifying data sets indicate that the proposed soft quadratic surface support vector machine model may outperform Dagher’s quadratic model and other soft support vector machine models with a Quadratic or Gaussian kernel in terms of the classification accuracy and robustness.

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MINIMAL CLASSIFICATION METHOD WITH ERROR-CORRECTING CODES FOR MULTICLASS RECOGNITION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001405004241 ◽

2005 ◽

Vol 19 (05) ◽

pp. 663-680 ◽

Cited By ~ 15

Author(s):

DAYAN MANOHAR SIVALINGAM ◽

NARENKUMAR PANDIAN ◽

JEZEKIEL BEN-ARIE

Keyword(s):

Binary Classification ◽

Classification Problem ◽

Error Correcting Codes ◽

Classification Method ◽

Support Vector ◽

Object Image ◽

Generalization Error ◽

Image Library ◽

Binary Classification Problem ◽

Binary Classifiers

In this work, we develop an efficient technique to transform a multiclass recognition problem into a minimal binary classification problem using the Minimal Classification Method (MCM). The MCM requires only log 2 N classifications whereas the other methods require much more. For the classification, we use Support Vector Machine (SVM) based binary classifiers since they have superior generalization performance. Unlike the prevalent one-versus-one strategy (the bottom-up one-versus-one strategy is called tournament method) that separates only two classes at each classification, the binary classifiers in our method have to separate two groups of multiple classes. As a result, the probability of generalization error increases. This problem is alleviated by utilizing error correcting codes, which results only in a marginal increase in the required number of classifications. However, in comparison to the tournament method, our method requires only 50% of the classifications and still similar performance can be attained. The proposed solution is tested with the Columbia Object Image Library (COIL). We also test the performance under conditions of noise and occlusion.

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Predicting Takeover Success Using Machine Learning Techniques

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v10i10.7264 ◽

2012 ◽

Vol 10 (10) ◽

pp. 547

Author(s):

Mei Zhang ◽

Gregory Johnson ◽

Jia Wang

Keyword(s):

Machine Learning ◽

Learning Community ◽

Binary Classification ◽

Classification Problem ◽

Machine Learning Techniques ◽

Success Prediction ◽

Support Vector ◽

Font Size ◽

Network Support ◽

Learning Techniques

A takeover success prediction model aims at predicting the probability that a takeover attempt will succeed by using publicly available information at the time of the announcement. We perform a thorough study using machine learning techniques to predict takeover success. Specifically, we model takeover success prediction as a binary classification problem, which has been widely studied in the machine learning community. Motivated by the recent advance in machine learning, we empirically evaluate and analyze many state-of-the-art classifiers, including logistic regression, artificial neural network, support vector machines with different kernels, decision trees, random forest, and Adaboost. The experiments validate the effectiveness of applying machine learning in takeover success prediction, and we found that the support vector machine with linear kernel and the Adaboost with stump weak classifiers perform the best for the task. The result is consistent with the general observations of these two approaches.

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Hierarchical Multi Twin Support Vector Machine

HUE UNIVERSITY JOURNAL OF SCIENCE TECHNIQUES AND TECHNOLOGY ◽

10.26459/hueunijtt.v130i2b.5829 ◽

2021 ◽

Vol 130 (2B) ◽

Author(s):

Nguyen The Cuong

Keyword(s):

Support Vector Machine ◽

Structural Information ◽

Binary Classification ◽

Classification Problem ◽

Data Type ◽

Twin Support Vector Machine ◽

Support Vector ◽

Classification Problems ◽

Data Simulation ◽

Better Than

In binary classification problems, two classes normally have different tendencies. More complex, the clusters in each class also tend to be different. Traditional algorithms as Support Vector Machine (SVM) or Twin Support Vector Machine (TWSVM) don't sufficiently exploit structural information with cluster granularity of the data, cause of restricts the capability of simulation of data trends. Structural twin support vector machine (S-TWSVM) sufficiently exploits structural information with cluster granularity of one class for learning a represented hyperplane of that class. This makes S-TWSVM's data simulation capabilities better than TWSVM. However, for the data type that each class consists of clusters of different trends, the capability of simulation of S-TWSVM is restricted. In this paper, we propose a new Hierarchical Multi Twin Support Vector Machine (called HM-TWSVM) for classification problem with each cluster-vs-class strategy. HM-TWSVM overcomes the limitations of S-TWSVM. Experiment results show that HM-TWSVM could describe the tendency of each cluster.

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Predicting Anesthetic Infusion Events Using Machine Learning

10.21203/rs.3.rs-783161/v1 ◽

2021 ◽

Author(s):

Naoki Miyaguchi ◽

Koh Takeuchi ◽

Hisashi Kashima ◽

Mizuki Morita ◽

Hiroshi Morimatsu

Keyword(s):

Machine Learning ◽

Flow Rate ◽

Short Term Memory ◽

Binary Classification ◽

Classification Problem ◽

Clinical Findings ◽

Support Vector ◽

Learning Models ◽

Continuous Administration ◽

Machine Learning Models

Abstract Recently, research has been conducted to automatically control anesthesia using machine learning, with the aim of alleviating the shortage of anesthesiologists. In this study, we address the problem of predicting decisions made by anesthesiologists during surgery using machine learning; specifically, we formulate a decision making problem by increasing the flow rate at each time point in the continuous administration of analgesic remifentanil as a supervised binary classification problem. The experiments were conducted to evaluate the prediction performance using six machine learning models: logistic regression, support vector machine, random forest, LightGBM, artificial neural network, and long short-term memory (LSTM), using 210 case data collected during actual surgeries. The results demonstrated that when predicting the future increase in flow rate of remifentanil after 1 min, the model using LSTM was able to predict with scores of 0.659 for sensitivity, 0.732 for specificity, and 0.753 for ROC-AUC; this demonstrates the potential to predict the decisions made by anesthesiologists using machine learning. Furthermore, we examined the importance and contribution of the features of each model using shapley additive explanations—a method for interpreting predictions made by machine learning models. The trends indicated by the results were partially consistent with known clinical findings.

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CANCER PROGNOSTIC EVALUATION VIA SUPPORT VECTOR MACHINES

International Journal of Computing ◽

10.47839/ijc.3.3.302 ◽

2014 ◽

pp. 29-34

Author(s):

Domenico Conforti ◽

Domenico Costanzo ◽

Rosita Guido

Keyword(s):

Decision Making ◽

Medical Decision Making ◽

Binary Classification ◽

Classification Problem ◽

Good Prognosis ◽

Medical Decision ◽

Support Vector ◽

Breast Cancer Patients ◽

Decision Making Problem

In this paper we considered a very challenging medical decision making problem: the short-term prognosis evaluation of breast cancer patients. In particular, the oncologist has to predict the more likely outcome of the disease in terms of survival or recurrence after a given follow-up period: “good” prognosis if the patient is still alive and has not recurrence after the follow-up period, “poor” prognosis if the patient has recurrence or dies within the follow-up period. This prediction can be realized on the basis of the execution of specific clinical tests and patient examinations. The relevant medical decision making problem has been formulated as a supervised binary classification problem. By the framework of generalized Support Vector Machine models, we tested and validate the behavior of four kernel based classifiers: Linear, Polynomial, Gaussian and Laplacian. The overall results demonstrate the effectiveness and robustness of the proposed approaches for solving the relevant medical decision making problem.

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Evaluation Of Machine Learning Tools For Distinguishing Fraud From Error

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v11i9.8067 ◽

2013 ◽

Vol 11 (9) ◽

pp. 393

Author(s):

Mei Zhang

Keyword(s):

Machine Learning ◽

Binary Classification ◽

Classification Problem ◽

General Purpose ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Learning Tools ◽

Pure Chance ◽

Error Classification

Fraud and error are two underlying sources of misstated financial statements. Modern machine learning techniques provide a potential direction to distinguish the two factors in such statements. In this paper, a thorough evaluation is conducted evaluation on how the off-the-shelf machine learning tools perform for fraud/error classification. In particular, the task is treated as a standard binary classification problem; i.e., mapping from an input vector of financial indices to a class label which is either error or fraud. With a real dataset of financial restatements, this study empirically evaluates and analyzes five state-of-the-art classifiers, including logistic regression, artificial neural network, support vector machines, decision trees, and bagging. There are several important observations from the experimental results. First, it is observed that bagging performs the best among these commonly used general purpose machine learning tools. Second, the results show that the underlying relationship from the statement indices to the fraud/error decision is likely to be non-linear. Third, it is very challenging to distinguish error from fraud, and general machine learning approaches, though perform better than pure chance, leave much room for improvement. The results suggest that more advanced or task-specific solutions are needed for fraud/error classification.

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WEIGHTED STRUCTURAL SUPPORT VECTOR MACHINE

Journal of Computer Science and Cybernetics ◽

10.15625/1813-9663/37/1/15396 ◽

2021 ◽

Vol 37 (1) ◽

pp. 43-56

Author(s):

Nguyen The Cuong ◽

Huynh The Phung

Keyword(s):

Support Vector Machine ◽

Structural Information ◽

Binary Classification ◽

Classification Problem ◽

Twin Support Vector Machine ◽

Support Vector ◽

Classification Problems ◽

Data Simulation ◽

Training Time ◽

Structural Support

In binary classification problems, two classes of data seem to be different from each other. It is expected to be more complicated due to the clusters in each class also tend to be different. Traditional algorithms as Support Vector Machine (SVM) or Twin Support Vector Machine (TWSVM) cannot sufficiently exploit structural information with cluster granularity of the data, cause limitation on the capability of simulation of data trends. Structural Twin Support Vector Machine (S-TWSVM) sufficiently exploits structural information with cluster granularity for learning a represented hyperplane. Therefore, the capability of S-TWSVM’s data simulation is better than that of TWSVM. However, for the datasets where each class consists of clusters of different trends, the S-TWSVM’s data simulation capability seems restricted. Besides, the training time of S-TWSVM has not been improved compared to TWSVM. This paper proposes a new Weighted Structural - Support Vector Machine (called WS-SVM) for binary classification problems with a class-vs-clusters strategy. Experimental results show that WS-SVM could describe the tendency of the distribution of cluster information. Furthermore, both the theory and experiment show that the training time of the WS-SVM for classification problem has significantly improved compared to S-TWSVM.

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Optimization of Injection-Molding Process Parameters for Weight Control: Converting Optimization Problem to Classification Problem

Advances in Polymer Technology ◽

10.1155/2020/7654249 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Peng Zhao ◽

Zhengyang Dong ◽

Jianfeng Zhang ◽

Yi Zhang ◽

Mingyi Cao ◽

...

Keyword(s):

Injection Molding ◽

Process Parameters ◽

Optimization Problem ◽

Classification Problem ◽

Support Vector ◽

Injection Molding Process ◽

Average Weight ◽

Optimization Strategy ◽

Molding Process ◽

Injection Molded

Product weight is one of the most important properties for an injection-molded part. The determination of process parameters for obtaining an accurate weight is therefore essential. This study proposed a new optimization strategy for the injection-molding process in which the parameter optimization problem is converted to a weight classification problem. Injection-molded parts are produced under varying parameters and labeled as positive or negative compared with the standard weight, and the weight error of each sample is calculated. A support vector classifier (SVC) method is applied to construct a classification hyperplane in which the weight error is supposed to be zero. A particle swarm optimization (PSO) algorithm contributes to the tuning of the hyperparameters of the SVC model in order to minimize the error between the SVC prediction results and the experimental results. The proposed method is verified to be highly accurate, and its average weight error is 0.0212%. This method only requires a small amount of experiment samples and thus can reduce cost and time. This method has the potential to be widely promoted in the optimization of injection-molding process parameters.

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Mushroom Toxicity Recognition Based on Multigrained Cascade Forest

Scientific Programming ◽

10.1155/2020/8849011 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Yingying Wang ◽

Jixiang Du ◽

Hongbo Zhang ◽

Xiuhong Yang

Keyword(s):

Support Vector Machine ◽

Logistic Regression ◽

Binary Classification ◽

Animal Experiments ◽

Food Poisoning ◽

Classification Problem ◽

Support Vector ◽

Automatic Identification ◽

Mushroom Species ◽

Toxicity Identification

Due to the tastiness of mushroom, this edible fungus often appears in people’s daily meals. Nevertheless, there are still various mushroom species that have not been identified. Thus, the automatic identification of mushroom toxicity is of great value. A number of methods are commonly employed to recognize mushroom toxicity, such as folk experience, chemical testing, animal experiments, and fungal classification, all of which cannot produce quick, accurate results and have a complicated cycle. To solve these problems, in this paper, we proposed an automatic toxicity identification method based on visual features. The proposed method regards toxicity identification as a binary classification problem. First, intuitive and easily accessible appearance data, such as the cap shape and color of mushrooms, were taken as features. Second, the missing data in any of the features were handled in two ways. Finally, three pattern-recognition methods, including logistic regression, support vector machine, and multigrained cascade forest, were used to construct 3 different toxicity classifiers for mushrooms. Compared with the logistic regression and support vector machine classifiers, the multigrained cascade forest classifier had better performance with an accuracy of approximately 98%, enhancing the possibility of preventing food poisoning. These classifiers can recognize the toxicity of mushrooms—even that of some unknown species—according to their appearance features and important social and application value.

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