https://doi.org/10.1371/journal.pone.0094137.g004. 4) uses the model M0. 2018;115:107. This result suggests that the default parameters usually provide a classification performance that is close to the optimum. Restivo MT et al. These values considered using the first scenario (universal use). The highest results were obtained using the classifier structure that employed the cascade models and the scenario of individual dataset. Most importantly, the investigation carried out in [31] warns that invalid conclusions can be drawn if specific statistical tests are not applied. This procedure was repeated 100 times for each object, resulting in a total of 800 sets of sensor inputs for each user (totalizing, for three users, 2400 sets of sensors inputs). Bhuvaneswari R, Subban R. Novel object detection and recognition system based on points of interest selection and SVM classification. Different patterns of aphasia are related to the location of the brain injury: global aphasia [6], Brocas aphasia [7], mixed non-fluent aphasia [8], Wernickes aphasia [9], primary progressive aphasia [10], and anomic aphasia [11], the patient has difficulty finding words, mainly nouns, and verbs, making continuous discourse difficult. The confusion matrix is used to identify the behaviour of a classifier on a given set of data for which the true valuesare known. Also shown on the last column is the mean accuracy of each classifier over all configurations. Sun N, Sun B, Lin JD, Wu MYC. /Obj3 3 0 R >> Python [22] was used as software to implement multiclass classifiers, and scikit-learn [23] was used as a library. }!ia%j=od*a6~&',8ie|7]zrx\G [20][24]); and (b) systematic qualitative and quantitative comparison between many representative classifiers. This is probably the most common way researchers use the software. Interestingly, it turns out that for comparing two general learning methods, the ratio of the training sample size and the evaluation size does not have to approach 0 for consistency in selection, as is required for comparing parametric regression models (Shao (1993)). The 5DT Glove provides data from its five sensors. U-shaped), the classes can exhibit all kinds of correlations. This effect becomes evident when one analyzes, for example, the kNN. A comparison of three representative learning methods (Naive Bayes, decision trees and SVM) was conducted in [35], concluding that Naive Bayes is significantly better than decision trees if the area under curve is employed as a performance measurement. (b) The accuracy rate for the default parameters are subtracted from the values obtained for the random drawing. Actually, the adequate choice of classifiers and parameters in such practical circumstances constitutes a long-standing problem and is one of the subjects of the current paper. Telemed e-Health. (a) kNN; (b) C4.5; (c) Multilayer Perceptron; (d) Logistic; (e) Random Forest; (f) Simple CART; (g) SVM. This model uses the data from the five sensors glove as features to classify the objects. Some studies in the literature are devoted to devising novel methodologies to analyze statistically the results obtained from the comparison of classifiers. As shown in Tables3 and4, the classifiers structures, Random Forest, Label Propagation and Label Spreading were the best classification techniques in terms of accuracy.
5). The supervised classifier uses a set of labeled data that has its known output. Besides, a large number of scenarios and activities can be implemented with different objectives applied in the treatment of the most different difficulties in the area of healthcare [2]. Cogn Syst Res. The database is running on a main server for storing the data from the three users and their e-rehabilitation activities. Yes
The kNN usually provides a larger accuracy than the Multilayer Perceptron, but in cases where the Multilayer Perceptron is better than the kNN, it becomes the best classifier. In addition, we also compute the variation of accuracy across datasets, as this quantity is useful to quantify the confidence of the classifier when the dataset is changed. As for the SVM classifier, six of its parameters allowed an increase of about 20%, which led to accuracy rates higher than 94% in many cases. The comparative analysis involves the following three approaches. of the kNN classifier. The idea behind this procedure is that fixing the accuracy rate of one classifier will likely avoid other classifiers from reaching too extreme values. Supporting continuity and open collaboration. Therefore, this work becomes relevant because it can be a building block for e-rehabilitation and e-training exercises used for aphasia recovering. https://doi.org/10.1007/s10115-007-0114-2. Therefore, in order to avoid a trivial result of all classifiers providing better results when including more features, for each dataset we set . However, the test time using Random Forest was shorter than using Label Propagation (14% in model M0 and 26% in the M1+M2) and using Label Spreading (17% in model M0 and 25% in the M1+M2). An example of the procedures adopted in the multidimensional analysis is provided in Figure 4. By comparing Tables 2 and 3 it is clear that although the Multilayer Perceptron usually outperforms other classifiers, it is not always by a large margin. Panel (a) illustrates the default value of the parameter () with a red vertical dashed line. For instance, Horvth et al. EVA Park. 8.
Perhaps this is a consequence of the No Free Lunch theorem, which states that, without any prior, no single method can be preferred [32][34]. 2016;10:595. The red dashed line indicates the performance achieved with default parameters. Ann Neurol. The multiclass classification techniques were tested in two scenarios and with two classifier structures, using three models. Finally, panel (c) displays the distribution of in DB2F. The software applications were built using the Unity engine, allowing support for different platforms, such as Windows, Android or iOS, enabling the development of a platform independent software. Nevertheless, we observe that the comparative performance of the methods present in this paper may change for other databases or conditions. Improved naming after TMS treatments in a chronic, global aphasia patientcase report. Others studies based on the use of technology applied to the treatment of aphasia are shown in Table1. For example, when increasing the number of features, if the values of remains fixed, we expect all classifiers to provide accuracies close to 100%. This item is part of a JSTOR Collection. As a result of this work, three classification techniques presented similar accuracies for the classification of objects.
Some studies show that many of the papers aiming at comparing the performance of different classifiers are limited in the sense that they compare several methods with respect to relatively few datasets [29], [30]. Ann N Y Acad Sci. Even if one manages to consistently compare the results obtained with hundreds of real world datasets, the results still remain specific to the datasets being used. Macoir J, Lavoie M, Routhier S, Bier N. Key factors for the success of self-administered treatments of poststroke aphasia using technologies. An extensive analysis comparing the quality achieved with the parameters set with default and non-default values is provided in Table 3 for classifications obtained in DB2F. 2012;13(3):17783. In particular, we decided to use Weka because of its popularity among researchers. Aphasia is defined as a communication difficulty caused by a focal or degenerative lesion in the areas of the brain responsible for the language, creating problems of expression, comprehension, reading, and writing [4]. /Parent 2 0 R The authors declare that they have no competing interests. In this section we present a generic methodology to construct artificial datasets modeling the different characteristics of real data. The application aims to identify a set of objects commonly used for a person through grasping. Currently, the developed applications support the use of instrumented gloves, handle devices and inertial devices for e-rehabilitation and e-training. <> Nevertheless, there are some measurements that have widespread use in the literature, the most popular being the accuracy rate, f-measure (sometimes together with precision and recall), Kappa statistic, ROC area under curve and the time spent for classification (see [53] for a comprehensive explanation of such measurements). Next, we introduce the measurements used to quantify the classifiers performance.
https://doi.org/10.1016/j.is.2018.05.006. Yang Y, Loog M. A benchmark and comparison of active learning for logistic regression. 2018;69:15361. The last column shows the percentage of configurations where the classifier reached the best rank. Thus, the classification is based on the existence of a relation between the input and output. The analysis of performance with default parameters in the artificial dataset revealed that the kNN usually outperforms the other methods. https://doi.org/10.1371/journal.pone.0094137.g005, https://doi.org/10.1371/journal.pone.0094137.t006, https://doi.org/10.1371/journal.pone.0094137.t007. Chao W, Junzheng W. Cloud-service decision tree classification for education platform. Figure9 shows the average normalized overall confusion matrices for the second classifier structure (M1+M2) using the second scenario (personalised use). to go back to the article page.Or contact our https://doi.org/10.1016/j.bdr.2018.05.007. <> The behavior (ii) is the most common trend. When just one parameter was allowed to vary, there was not a large variation in the accuracy compared with the classification achieved with default parameters. Note that the Naive Bayes and Bayesian Net classifiers were not included in the multidimensional analysis, since they only have binary parameters. Evaluating the effects of a virtual communication environment for people with aphasia. Many techniques have been devised to tackle such a diversity of applications. Google Scholar. A significant improvement in the discriminability was observed for the Multilayer Perceptron through the variation of the size of the hidden layers (H). In an attempt to circumvent such problem and to obtain more robust and versatile classifiers, a number of pattern recognition methods have been proposed in the literature [11][13]. The difference between these two quantities is represented by .
