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Machine Learning
Published in Seyedeh Leili Mirtaheri, Reza Shahbazian, Machine Learning Theory to Applications, 2022
Seyedeh Leili Mirtaheri, Reza Shahbazian
Now, you are able to train a SVM classifier. But there are two other issues that we want to discuss about them. One issue you may notice is that the SVM classifier is a binary classifier and does not support multiclass classification natively. It only supports binary classification and separating data points into two classes. For multiclass classification, the same principle is utilized after breaking down the multiclass classification problem into multiple binary classification problems. The second issue is the kernel trick. As you may notice, the SVM classifier can separate only linear data points like shown in the above figure. For the nonlinear data point (or features), you can pass the input features from a kernel and map them to a new space which the data points can separate linearly. In the following we will focus on these issues.
Deployment of Supervised Machine Learning and Deep Learning Algorithms in Biomedical Text Classification
Published in Saravanan Krishnan, Ramesh Kesavan, B. Surendiran, G. S. Mahalakshmi, Handbook of Artificial Intelligence in Biomedical Engineering, 2021
G. Kumaravelan, Bichitrananda Behera
The classifier model build using supervised ML algorithms are broadly divided into two forms, namely multiclass and multilabel classification. The multiclass classification is the one where a single class label out of many is assigned to one instance. Decision tree (DT) classifier, k-nearest neighborhood (k-NN) classifier, Rocchio classifier (RC), ridge classifier, passive–aggressive (PA) classifier, multinomial naïve Bayes (M_NB) classifier, Bernoulli naïve Bayes (B_NB) classifier, support vector machine (SVM) classifier, artificial neural network (ANN) classifier including perceptron (PPN), stochastic gradient descent (SGD), BPN are the most prominent classifier found in the literature of supervised ML community. However, multilabel classification assigns more than one class labels among the instances, and it is considered to be more complex classification than multiclass classification. Specifically, multilabel classification falls into two main categories, namely problem adaption and algorithm adaption. Problem adaptation method transforms the multilabel problem into a single-label or multiclass problem(s). The main aim of this type of transformation is to fit the data to the multiclass algorithm.
Tree-Walk Kernels for Computer Vision
Published in Olivier Lézoray, Leo Grady, Image Processing and Analysis with Graphs, 2012
For the multi-class classification task, the usual SVM classifier was used in a one-versus-all setting [1]. For each family of kernels, hyper-parameters corresponding to kernel design and the SVM regularization parameter C were learned by crossvalidation, with the following usual machine learning procedure: we randomly split the full dataset in 5 parts of equal size, then we consider successively each of the 5 parts as the testing set (the outer testing fold), learning being performed on the four other parts (the outer training fold). This is in contrast to other evaluation protocols. Assume instead one is trying out different values of the free parameters on the outer training fold, computing the prediction accuracy on the corresponding testing fold, repeating five times for each of the five outer folds, and compute average performance. Then assume one selects the best hyper-parameter and report its performance. A major issue with such a protocol is that it leads to an optimistic estimation of the prediction performance [49].
Empirical Analysis of Machine Learning Algorithms on Detection of Fraudulent Electronic Fund Transfer Transactions
Published in IETE Journal of Research, 2022
A. Asad Arfeen, B. Muhammad Asim Khan
The intrusion detection dataset consists of five classes. One class is normal while the other four classes are different intrusion classes (DoS, Probe, R2L, and U2R). We tested three models; ANN, random forest and softmax regression by assuming binary and multi-class classification. In binary classification, data are labelled into two classes; normal or intrusion (all four different intrusions combined as a single class). In multiclass classification, data are classified into more than two classes. Since the chosen dataset includes five classes, we performed a 5-class classification. Figure 4 displays the AUC-ROC curve of all three classifiers using binary classification. It can be observed that softmax regression and ANN shows the AUC score of 0.854 and 0.850, respectively, while the random forest is displaying a 0.804 AUC score.
Classification of Deep-SAT Images under Label Noise
Published in Applied Artificial Intelligence, 2021
Mohammad Minhazul Alam, Md Gazuruddin, Nahian Ahmed, Abdul Motaleb, Masud Rana, Romman Riyadh Shishir, Sabrina Yeasmin, Rashedur M. Rahman
SVC separates classes by generating a hyper-plane. To separate the data linearly, they are embedded into higher dimensions if needed. Depending on the classification scenario it can use different kernel functions like Linear, or Radial Basis function. In SVC implementation, there are some parameters that can be adjusted to get better result out of it. A typical Support Vector Classifier implementation can usually classify between two different classes only using a single hyper-plane. In our dataset however, there are six different classes. In order to achieve multi class classification using Support Vector Machines, there are two ways in scikit-learn – the one-vs-one and the one-vs-many classifiers. In the one-vs-one classifier, a separate SVC is trained for each of the NC2 choice of class labels. This is time consuming and the one-vs-many classifier is usually preferable. Once a test instance is received, the classifier that successfully classify the instance positively with highest confidence value will be selected.
Detection and Classification of Corrosion-related Damage Using Solitary Waves
Published in Research in Nondestructive Evaluation, 2022
Hoda Jalali, Ritesh Misra, Samuel J. Dickerson, Piervincenzo Rizzo
The effectiveness of each classification model was evaluated using the accuracy metric, which is one of the most popular metrics in multi-class classification. Accuracy is computed using the confusion matrix, i.e., a table that presents the actual class vs the predicted class. Accuracy is computed as the ratio of correct classifications (on the main diagonal of the confusion matrix) to all the cases (all the entries of the confusion matrix) [58]. In fact, accuracy represents the probability that the model prediction is correct. For the binary classification models, the sensitivity was also computed as the ratio of the cases that the event of interest (unserviceable condition) was predicted correctly to the number of all cases that had the unserviceable condition [58].