Towards Emotion Recognition: A Persistent Entropy Application

Emotion recognition and classification is a very active area of research. In this paper, we present a first approach to emotion classification using persistent entropy and support vector machines. A topology-based model is applied to obtain a single real number from each raw signal. These data are used as input of a support vector machine to classify signals into 8 different emotions (calm, happy, sad, angry, fearful, disgust and surprised)

Towards Emotion Recognition: A Persistent Entropy Application

Emotion recognition and classification is a very active area of research. In this paper, we present a first approach to emotion classification using persistent entropy and support vector machines. A topology-based model is applied to obtain a single real number from each raw signal. These data are used as input of a support vector machine to classify signals into 8 different emotions (calm, happy, sad, angry, fearful, disgust and surprised)

Active Learning for Dialogue Act Classification

Active learning techniques were employed for classification of dialogue acts over two dialogue corpora, the English human-human Switchboard corpus and the Spanish human-machine Dihana corpus. It is shown clearly that active learning improves on a baseline obtained through a passive learning approach to tagging the same data sets. An error reduction of 7% was obtained on Switchboard, while a factor 5 reduction in the amount of labeled data needed for classification was achieved on Dihana. The passive Support Vector Machine learner used as baseline in itself significantly improves the state of the art in dialogue act classification on both corpora. On Switchboard it gives a 31% error reduction compared to the previously best reported result

Urban sprawl analysis in Kutupalong Refugee Camp, Bangladesh

Dissertation presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and ScienceUrban sprawling is a common phenomenon associated with geographical and political challenges such as refugee settlements and environmental extremes. Urban sprawl related to refugee or habitation settlement has been an area of active interest because of humanitarian and environmental problems. For example, higher rates of urban sprawling are positively correlated with higher rates of deforestation. The present study explored the viability and reproducibility of different classification techniques in assessing urban sprawl among Rohingya refugees in the Kutupalong refugee camp in South-Eastern Bangladesh. Two classification techniques were used to assess the urban sprawl among the study population. These classifications include the Support Vector Machine and Maximum Likelihood Classifier. The sprawl was measured based on the classification of urban ad non-urban classes, according to the topography of the camps. The study showed that urban class exhibited exponential growth from 2.01 km2 to 5.37 km2 within nine months based on Support Vector Machine Classifier, while Maximum Likelihood Classification detected 3.2 km2 to 7.8 km2 of urbanization. On the contrary, the non-urban class shrunk from 12.58 km2 to 9.95 km2 during the same period with Support Vector Machine and 11.3 km2 to 6.7 km2 with Maximum Likelihood Classification. The Support Vector Machine yielded better overall accuracy performance compared to Maximum Likelihood Classification

The Classification of Anxiety, Depression, and Stress on Facebook Users Using the Support Vector Machine

Social media remains an essential platform for connecting people with friends, family, and the world around them. However, when events spread on social media are primarily negative, it will cause depression, anxiety, and stress that tend to increase. This study aims to classify depression, anxiety, and stress using the Support Vector Machine. The data in this study were obtained from active Facebook users using the Depression Anxiety Stress Scale (DASS 21) questionnaire. This study adopted the Knowledge Discover Database process. The result of this study is an evaluation of the performance of the Support Vector Machine classification of depression, anxiety, and stress. The accuracy of the Support Vector Machine in this study is 98.96%

ANALISIS DAN IMPLEMENTASI ACTIVE LEARNING PADA SUPPORT VECTOR MACHINE

ABSTRAKSI: Pada supervised machine learning, data latih yang telah diberi label yang benar merupakan suatu hal yang menjadi prasyarat. Namun, dalam banyak pengaplikasiannya, tugas memberi label tidak bisa dilakukan secara otomatis, tapi melibatkan keputusan manusia dan oleh karena itu membutuhkan waktu yang banyak dan mahal.Pada tugas akhir ini, active learning diimplementasikan pada support vector machine dan diteliti apa faktor yang mempengaruhi jumlah data latih yang diberi label dan tingkat akurasi sistem, dan bagaimana pengaruhnya. Selain itu juga dibandingkan metode pemilihan inisial data dan next data, yaitu metode random dan metode dissimilarity. Data yang digunakan dalam tugas akhir ini adalah Winsconsin Breast Cancer Diagnosis dan Hill-Valley dari UCI Repository. Tujuan utama dari active learning adalah memilih data yang penting atau berpengaruh pada sistem, sehingga bisa mengurangi jumlah data yang perlu diberi label.Hasil penelitian adalah active learning mampu mengurangi jumlah data yang harus diberi label sampai 82.5% tanpa terjadi penurunan akurasi sistem yang significant.Kata Kunci : Active Learning, Support Vector Machine, klasifikasi, label data, pengurangan.ABSTRACT: In supervised machine learning, a training set of examples which are assigned to the correct target labels is a necessary prerequisite. However, in many applications, the task of assigning target labels cannot be conducted in an automatic manner, but involves human decisions and is therefore time-consuming and expensive.In this final task, active learning is implemented in a support vector machine and examined what factors affect the amount of labeled training data and the accuracy of the system, and how they affect. It also compared the selection method of initial data and next data, the random method and the dissimilarity method. Data used in this final task is the Wisconsin Breast Cancer Diagnosis and Hill-Valley from the UCI Repository. The main goal of active learning is to select the data that is important or have influence in the system, so that it can reduce the amount of data that need to be labeled.The results showed that active learning can reduce the amount of data need tobe labeled up to 82.5% without any significant decrease in the system accuracy.Keyword: Active Learning, Support Vector Machine, classification, data label, reduction

Robust optimization of SVM hyperparameters in the classification of bioactive compounds

Background: Support Vector Machine has become one of the most popular machine learning tools used in vir - tual screening campaigns aimed at finding new drug candidates. Although it can be extremely effective in finding new potentially active compounds, its application requires the optimization of the hyperparameters with which the assessment is being run, particularly the C and γ values. The optimization requirement in turn, establishes the need to develop fast and effective approaches to the optimization procedure, providing the best predictive power of the constructed model. Results: In this study, we investigated the Bayesian and random search optimization of Support Vector Machine hyperparameters for classifying bioactive compounds. The effectiveness of these strategies was compared with the most popular optimization procedures—grid search and heuristic choice. We demonstrated that Bayesian optimiza- tion not only provides better, more efficient classification but is also much faster—the number of iterations it required for reaching optimal predictive performance was the lowest out of the all tested optimization methods. Moreover, for the Bayesian approach, the choice of parameters in subsequent iterations is directed and justified; therefore, the results obtained by using it are constantly improved and the range of hyperparameters tested provides the best over - all performance of Support Vector Machine. Additionally, we showed that a random search optimization of hyperpa- rameters leads to significantly better performance than grid search and heuristic-based approaches. Conclusions: The Bayesian approach to the optimization of Support Vector Machine parameters was demonstrated to outperform other optimization methods for tasks concerned with the bioactivity assessment of chemical com- pounds. This strategy not only provides a higher accuracy of classification, but is also much faster and more directed than other approaches for optimization. It appears that, despite its simplicity, random search optimization strategy should be used as a second choice if Bayesian approach application is not feasible

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches