Penerapan Ensemble Stacking untuk Klasifikasi Multi Kelas

Klasifikasi adalah salah satu topik utama yang banyak digunakan dalam penelitian pembelajaran mesin. Beberapa penelitian terdahulu telah menghasilkan base classifier yang sampai saat ini masih digunakan. Banyak base classifier menunjukkan performa yang baik untuk klasifikasi biner tetapi performa classifier tersebut menurun pada saat digunakan untuk klasifikasi multi-kelas. Pada penelitian sebelumnya digunakan hybrid classifier untuk klasifikasi multi kelas. Hasil penelitian menunjukkan akurasi hybrid classifier yang diajukan lebih baik dari base classifier. pada penelitian ini ensemble method stacking diterapkan. Decision tree dan naïve bayes digunakan sebagai classifier dasar. Hasil pengujian menunjukkan metode ensemble stacking hanya mampu melampui pada beberapa dataset jika dibandingkan dengan hybrid classifier

Penerapan Ensemble Stacking Untuk Klasifikasi Multi Kelas

Klasifikasi adalah salah satu topik utama yang banyak digunakan dalam penelitian pembelajaran mesin. Beberapa penelitian terdahulu telah menghasilkan base classifier yang sampai saat ini masih digunakan. Banyak base classifier menunjukkan performa yang baik untuk klasifikasi biner tetapi performa classifier tersebut menurun pada saat digunakan untuk klasifikasi multi-kelas. Pada penelitian sebelumnya digunakan hybrid classifier untuk klasifikasi multi kelas. Hasil penelitian menunjukkan akurasi hybrid classifier yang diajukan lebih baik dari base classifier. pada penelitian ini ensemble method stacking diterapkan. Decision tree dan naïve bayes digunakan sebagai classifier dasar. Hasil pengujian menunjukkan metode ensemble stacking hanya mampu melampui pada beberapa dataset jika dibandingkan dengan hybrid classifier

Classifier Subset Selection to construct multi-classifiers by means of estimation of distribution algorithms

This paper proposes a novel approach to select the individual classifiers to take part in a Multiple-Classifier System. Individual classifier selection is a key step in the development of multi-classifiers. Several works have shown the benefits of fusing complementary classifiers. Nevertheless, the selection of the base classifiers to be used is still an open question, and different approaches have been proposed in the literature. This work is based on the selection of the appropriate single classifiers by means of an evolutionary algorithm. Different base classifiers, which have been chosen from different classifier families, are used as candidates in order to obtain variability in the classifications given. Experimental results carried out with 20 databases from the UCI Repository show how adequate the proposed approach is; Stacked Generalization multi-classifier has been selected to perform the experimental comparisons.The work described in this paper was partially conducted within the Basque Government Research Team grant and the University of the Basque Country UPV/EHU and under grant UFI11/45 (BAILab)

Ensemble of 6 DoF Pose estimation from state-of-the-art deep methods.

Deep learning methods have revolutionized computer vision since the appearance of AlexNet in 2012. Nevertheless, 6 degrees of freedom pose estimation is still a difficult task to perform precisely. Therefore, we propose 2 ensemble techniques to refine poses from different deep learning 6DoF pose estimation models. The first technique, merge ensemble, combines the outputs of the base models geometrically. In the second, stacked generalization, a machine learning model is trained using the outputs of the base models and outputs the refined pose. The merge method improves the performance of the base models on LMO and YCB-V datasets and performs better on the pose estimation task than the stacking strategy.This paper has been supported by the project PROFLOW under the Basque program ELKARTEK, grant agreement No. KK-2022/00024

Evolving interval-based representation for multiple classifier fusion.

Designing an ensemble of classifiers is one of the popular research topics in machine learning since it can give better results than using each constituent member. Furthermore, the performance of ensemble can be improved using selection or adaptation. In the former, the optimal set of base classifiers, meta-classifier, original features, or meta-data is selected to obtain a better ensemble than using the entire classifiers and features. In the latter, the base classifiers or combining algorithms working on the outputs of the base classifiers are made to adapt to a particular problem. The adaptation here means that the parameters of these algorithms are trained to be optimal for each problem. In this study, we propose a novel evolving combining algorithm using the adaptation approach for the ensemble systems. Instead of using numerical value when computing the representation for each class, we propose to use the interval-based representation for the class. The optimal value of the representation is found through Particle Swarm Optimization. During classification, a test instance is assigned to the class with the interval-based representation that is closest to the base classifiers’ prediction. Experiments conducted on a number of popular dataset confirmed that the proposed method is better than the well-known ensemble systems using Decision Template and Sum Rule as combiner, L2-loss Linear Support Vector Machine, Multiple Layer Neural Network, and the ensemble selection methods based on GA-Meta-data, META-DES, and ACO

Classifying Imbalanced Data Sets by a Novel RE-Sample and Cost-Sensitive Stacked Generalization Method

Learning with imbalanced data sets is considered as one of the key topics in machine learning community. Stacking ensemble is an efficient algorithm for normal balance data sets. However, stacking ensemble was seldom applied in imbalance data. In this paper, we proposed a novel RE-sample and Cost-Sensitive Stacked Generalization (RECSG) method based on 2-layer learning models. The first step is Level 0 model generalization including data preprocessing and base model training. The second step is Level 1 model generalization involving cost-sensitive classifier and logistic regression algorithm. In the learning phase, preprocessing techniques can be embedded in imbalance data learning methods. In the cost-sensitive algorithm, cost matrix is combined with both data characters and algorithms. In the RECSG method, ensemble algorithm is combined with imbalance data techniques. According to the experiment results obtained with 17 public imbalanced data sets, as indicated by various evaluation metrics (AUC, GeoMean, and AGeoMean), the proposed method showed the better classification performances than other ensemble and single algorithms. The proposed method is especially more efficient when the performance of base classifier is low. All these demonstrated that the proposed method could be applied in the class imbalance problem

Automatic Building of a Powerful IDS for The Cloud Based on Deep Neural Network by Using a Novel Combination of Simulated Annealing Algorithm and Improved Self- Adaptive Genetic Algorithm

Cloud computing (CC) is the fastest-growing data hosting and computational technology that stands today as a satisfactory answer to the problem of data storage and computing. Thereby, most organizations are now migratingtheir services into the cloud due to its appealing features and its tangible advantages. Nevertheless, providing privacy and security to protect cloud assets and resources still a very challenging issue. To address the aboveissues, we propose a smart approach to construct automatically an efficient and effective anomaly network IDS based on Deep Neural Network, by using a novel hybrid optimization framework “ISAGASAA”. ISAGASAA framework combines our new self-adaptive heuristic search algorithm called “Improved Self-Adaptive Genetic Algorithm” (ISAGA) and Simulated Annealing Algorithm (SAA). Our approach consists of using ISAGASAA with the aim of seeking the optimal or near optimal combination of most pertinent values of the parametersincluded in building of DNN based IDS or impacting its performance, which guarantee high detection rate, high accuracy and low false alarm rate. The experimental results turn out the capability of our IDS to uncover intrusionswith high detection accuracy and low false alarm rate, and demonstrate its superiority in comparison with stateof-the-art methods

Contributions on distance-based algorithms, multi-classifier construction and pairwise classification

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