Penerapan Adaboost untuk Klasifikasi Support Vector Machine Guna Meningkatkan Akurasi pada Diagnosa Chronic Kidney Disease

Database masa kini berkembang dengan sangat pesat khususnya dalam bidang kesehatan. Data tersebut apabila tidak diolah dengan baik maka akan menjadi sebuah tumpukan data yang tidak bermanfaat, sehingga perlu adanya proses untuk mengolah data tersebut menjadi sebuah informasi yang bermanfaat. Proses tersebut biasa disebut dengan data mining yang merupakan suatu bidang ilmu penelitian yang mampu mengolah database menjadi pengetahuan yang dapat dimanfaatkan khusunya dalam penelitian ini akan digunakan untuk mendiagnosa penyakit, diantaranya chronic kidney disease. Salah satu metode data mining yang digunakan untuk memprediksi sebuah keputusan dalam suatu hal adalah klasifikasi, di mana dalam metode klasifikasi ada algoritma support vector machine yang bisa digunakan untuk mendiagnosa chronic kidney disease. Dalam penelitian ini untuk meningkatkan akurasi algoritma support vector machine dalam mendiagnosa chronic kidney disease menggunakan adaptive boosting (adaboost) sebagai ensemble learning dengan pemilihan kernel, nilai parameter C, dan iterasi yang sesuai. Dari hasil percobaan, menerapkan adaboost, dengan kernel linier dan pemilihan nilai parameter C pada algoritma support vector machine dalam mendiagnosa chronic kidney disease menunjukkan bahwa tingkat akurasi mempunyai peningkatan sebesar 37% dengan pemaparan hasil seperti berikut, 62,5% (SVM); 97,75% (SVM+linier kernel); 99,5% (SVM+linier kernel +adaboost). Kata Kunci: adaboost, data mining, SVM, Adaptive boosting, chronic kidney diseas

Determination of protein-protein interaction through Artificial Neural Network and Support Vector Machine: A Comparative study

Protein-protein interactions (PPI) plays considerable role in most of the cellular processes and study of PPI enhances understanding of molecular mechanism of the cells. After emergence of proteomics, huge amount of protein sequences were generated but there interaction patterns are still unrevealed. Traditionally various techniques were used to predict PPI but are deficient in terms of accuracy. To overcome the limitations of experimental approaches numerous computational approaches were developed to find PPI. However previous computational approaches were based on descriptors, various external factors and protein sequences. In this article, a sequence based prediction model is proposed by using various machine learning approaches. A comparative study was done to understand efficiency of various machine learning approaches. Large amount of yeast PPI data have been analyzed. Same data has been incorporated for different classification approach like Artificial Neural Network (ANN) and Support Vector Machine (SVM), and compared their results. Existing methods with additional features were implemented to enhance the accuracy of the result. Thus it was concluded that efficiency of this model was more admirable than those existing sequence-based methods; therefore it can be effective for future proteomics research work

RVM-based adaboost scheme for stator interturn faults of the induction motor

This paper presents an AdaBoost method based on RVM (Relevance Vector Machine) to detect and locate an interturn short circuit fault in the stator windings of IM (Induction Machine). This method is achieved through constructing an Adaboost combined with a weak RVM multiclassifier based on a binary tree, and the fault features are extracted from the three phase shifts between the line current and the phase voltage of IM by establishing a global stator faulty model. The simulation results show that, compared with other competitors, the proposed method has a higher precision and a stronger generalization capability, and it can accurately detect and locate an interturn short circuit fault, thus demonstrating the effectiveness of the proposed method

Deep Learning Methods for Register Classification

For this project the data used is the one collected by, Biber and Egbert (2018) related to various language articles from the internet. I am using BERT model (Bidirectional Encoder Representations from Transformers), which is a deep neural network and FastText, which is a shallow neural network, as a baseline to perform text classification. Also, I am using Deep Learning models like XLNet to see if classification accuracy is improved. Also, it has been described by Biber and Egbert (2018) what is register. We can think of register as genre. According to Biber (1988), register is varieties defined in terms of general situational parameters. Hence, it can be inferred that there is a close relation between the language and the context of the situation in which it is being used. This work attempts register classification using deep learning methods that use attention mechanism. Working with the models, dealing with the imbalanced datasets in real life problems, tuning the hyperparameters for training the models was accomplished throughout the work. Also, proper evaluation metrics for various kind of data was determined. The background study shows that how cumbersome the use classical Machine Learning approach used to be. Deep Learning, on the other hand, can accomplish the task with ease. The metric to be selected for the classification task for different types of datasets (balanced vs imbalanced), dealing with overfitting was also accomplished

BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation

Feature-based matrix factorization techniques such as Factorization Machines (FM) have been proven to achieve impressive accuracy for the rating prediction task. However, most common recommendation scenarios are formulated as a top-N item ranking problem with implicit feedback (e.g., clicks, purchases)rather than explicit ratings. To address this problem, with both implicit feedback and feature information, we propose a feature-based collaborative boosting recommender called BoostFM, which integrates boosting into factorization models during the process of item ranking. Specifically, BoostFM is an adaptive boosting framework that linearly combines multiple homogeneous component recommenders, which are repeatedly constructed on the basis of the individual FM model by a re-weighting scheme. Two ways are proposed to efficiently train the component recommenders from the perspectives of both pairwise and listwise Learning-to-Rank (L2R). The properties of our proposed method are empirically studied on three real-world datasets. The experimental results show that BoostFM outperforms a number of state-of-the-art approaches for top-N recommendation