METODE ADABOOST PADA SKEMA PEMODELAN HYBRID UNTUK KLASIFIKASI PENYAKIT LIVER

Penelitian ini mengajukan perbandingan antara dua model hybrid yaitu, Artificial Neural Network (ANN) dan Adaboost pada skema pemodelan hybrid untuk klasifikasi penyakit liver. Digunakan metode Logistic Regression (LR), Multivariate Adaptive Regression Splines (MARS), Rough Set (RS), Artificial Neural Network (ANN), dan Adaboost untuk membangun skema model hybrid. Ada dua tahapan utama dalam penelitian ini, tahapan pertama menggunakan LR, MARS, dan RS untuk memilih fitur yang relevan terhadap klasifikasi dan selanjutnya fitur-fitur terpilih akan digunakan sebagai masukan pada klasifikasi menggunakan classifier ANN dan Adaboost. Tahap kedua adalah membangun skema hybrid yang menghasilkan enam kombinasi yaitu LR-ANN, MARS-ANN, RS-ANN, LR-Adaboost, MARS-Adaboost, dan RS-Adaboost. Penelitian ini juga membandingkan akurasi menggunakan classifier tunggal dengan model skema hybrid untuk klasifikasi penyakit liver. Secara keseluruhan, dengan menggunakan skema hybrid Adaboost, akurasi meningkat terhadap classifier tunggal Adaboost

Classification hardness for supervised learners on 20 years of intrusion detection data

This article consolidates analysis of established (NSL-KDD) and new intrusion detection datasets (ISCXIDS2012, CICIDS2017, CICIDS2018) through the use of supervised machine learning (ML) algorithms. The uniformity in analysis procedure opens up the option to compare the obtained results. It also provides a stronger foundation for the conclusions about the efficacy of supervised learners on the main classification task in network security. This research is motivated in part to address the lack of adoption of these modern datasets. Starting with a broad scope that includes classification by algorithms from different families on both established and new datasets has been done to expand the existing foundation and reveal the most opportune avenues for further inquiry. After obtaining baseline results, the classification task was increased in difficulty, by reducing the available data to learn from, both horizontally and vertically. The data reduction has been included as a stress-test to verify if the very high baseline results hold up under increasingly harsh constraints. Ultimately, this work contains the most comprehensive set of results on the topic of intrusion detection through supervised machine learning. Researchers working on algorithmic improvements can compare their results to this collection, knowing that all results reported here were gathered through a uniform framework. This work's main contributions are the outstanding classification results on the current state of the art datasets for intrusion detection and the conclusion that these methods show remarkable resilience in classification performance even when aggressively reducing the amount of data to learn from

Deep Neural Networks for Bot Detection

The problem of detecting bots, automated social media accounts governed by software but disguising as human users, has strong implications. For example, bots have been used to sway political elections by distorting online discourse, to manipulate the stock market, or to push anti-vaccine conspiracy theories that caused health epidemics. Most techniques proposed to date detect bots at the account level, by processing large amount of social media posts, and leveraging information from network structure, temporal dynamics, sentiment analysis, etc. In this paper, we propose a deep neural network based on contextual long short-term memory (LSTM) architecture that exploits both content and metadata to detect bots at the tweet level: contextual features are extracted from user metadata and fed as auxiliary input to LSTM deep nets processing the tweet text. Another contribution that we make is proposing a technique based on synthetic minority oversampling to generate a large labeled dataset, suitable for deep nets training, from a minimal amount of labeled data (roughly 3,000 examples of sophisticated Twitter bots). We demonstrate that, from just one single tweet, our architecture can achieve high classification accuracy (AUC > 96%) in separating bots from humans. We apply the same architecture to account-level bot detection, achieving nearly perfect classification accuracy (AUC > 99%). Our system outperforms previous state of the art while leveraging a small and interpretable set of features yet requiring minimal training data

ANN and Adaboost application for automatic detection of microcalcifications in breast cancer

AbstractObjectiveMicrocalcifications or MCs are considered to be the basic symptoms present in mammograms for breast cancer diagnosis. Therefore, the accurate detection of MCs is mandatory for the on-time diagnosis, effective treatment and reduction of mortality rates due to breast cancer. Mammogram analysis and interpretation is a challenging task, and there are many obstructions to the accurate detection of MCs such as small and non-uniform shape and size of the MCs clusters in addition to low contrast quality of MCs as compared to the rest of the tissue. These shortcomings of manual interpretation of MCs raise the need for an automatic detection system to assist radiologists in mammogram analysis. In this study, an automated system has been developed to minimize the manual inference and diagnose breast cancer with good precision. In this paper, we propose a two-fold detection algorithm. In the first stage, all suspicious regions from the mammogram are segmented out. In the next stage, these suspected regions are fed to a classifier which then detects whether the region was normal, benign or malignant. We compared the performance of a Neural Network classifier with Adaboost. ANN classifier shows more sensitivity and specificity but less accuracy as compared to Adaboost for tested images. Overall results show that the developed algorithm is able to achieve high accuracy and efficiency for the detection and diagnosis of breast cancer lesions for images from two different databases used, and also for mammograms obtained from a local hospital.ConclusionThe suggested algorithm was tested for DDSM, MIAS and local database and showed high level of overall accuracy (98.68%) and sensitivity (80.15%)

Credit card fraud detection using AdaBoost and majority voting

Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards