Neuroevolution untuk optimalisasi parameter jaringan saraf tiruan

Artificial Neural Network is a supervised learning method for various classification problems. Artificial Neural Network uses training data to identify patterns in the data; therefore, training phase is crucial. During this stage, the network weight is adjusted so that they can recognize patterns in the data. In this research, a neuroevolution approach is proposed to optimize artificial neural network parameters (weight) Neuroevolution is a combination of evolutionary algorithms, including various metaheuristics algorithms, to optimize neural network parameters and configuration. In particular, this research implemented particle swarm optimization as the artificial neural network optimizer. The performance of the proposed model was compared to backpropagation, which uses gradient information to adjust the neural network parameter. There are five datasets used as the benchmark problems. The datasets are iris, wine, breast cancer, ecoli, and wheat seeds. The experiment results show that the proposed method has better accuracy than the backpropagation in three out of five problems and has the same accuracy in two problems. The proposed method is also faster than the backpropagation method in all problems. These results reveal that neuroevolution is a promising approach to improving the performance of artificial neural networks. Further studies are needed to explore more benefits of this approach.Jaringan saraf tiruan merupakan metode supervised learning yang telah diterapkan untuk menyelesaikan berbagai permasalahan klasifikasi. Sebagai metode supervised learning, jaringan saraf tiruan memerlukan data training untuk mengidentifikasi pola dalam data sehingga fase learning menjadi penting. Pada fase learning, konfigurasi bobot pada jaringan saraf tiruan diatur sehingga jaringan saraf tiruan tersebut bisa mengenali pola di dalam data. Pada penelitian ini diusulkan metode untuk mengoptimalkan nilai bobot pada konfigurasi jaringan saraf tiruan menggunakan pendekatan neuroevolution. Neuroevolution adalah pengintegrasian metode evolutionary algorithm; termasuk di dalamnya adalah berbagai metode metaheuristik; dengan  jaringan saraf tiruan. Secara khusus, penelitian ini menggunakan metode particle swarm optimization untuk mengoptimalkan bobot pada jaringan saraf tiruan. Kinerja model yang diusulkan dibandingkan dengan metode backpropagation dengan stochastic gradient descent menggunakan lima dataset: iris, wine, breast cancer, ecoli, dan wheat seeds. Hasil eksperimen menunjukkan bahwa model yang diusulkan memiliki akurasi yang lebih baik di tiga dataset dari lima dataset dan memiliki kinerja yang sama di dua dataset. Hasil penelitian ini mengindikasikan bahwa pendekatan neuroevolution memiliki potensi sebagai metode optimalisasi parameter pada jaringan saraf tiruan. Penelitian ini bisa dikembangkan dengan mengidentifikasi karakteristik konvergensi dari pendekatan neuroevolution maupun menerapkan berbagai metode evolutionary algorithm untuk mengoptimalkan nilai bobot pada jaringan saraf tiruan

An Iterated Greedy Algorithm for Improving the Generation of Synthetic Patterns in Imbalanced Learning

Real-world classification datasets often present a skewed distribution of patterns, where one or more classes are under-represented with respect to the rest. One of the most successful approaches for alleviating this problem is the generation of synthetic minority samples by convex combination of available ones. Within this framework, adaptive synthetic (ADASYN) sampling is a relatively new method which imposes weights on minority examples according to their learning complexity, in such a way that difficult examples are more prone to be oversampled. This paper proposes an improvement of the ADASYN method, where the learning complexity of these patterns is also used to decide which sample of the neighbourhood is selected. Moreover, to avoid suboptimal results when performing the random convex combination, this paper explores the application of an iterative greedy algorithm which refines the synthetic patterns by repeatedly replacing a part of them. For the experiments, six binary datasets and four over-sampling methods are considered. The results show that the new version of ADASYN leads to more robust results and that the application of the iterative greedy metaheuristic significantly improves the quality of the generated patterns, presenting a positive effect on the final classification model

Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface

© 2020 IEEE. Kohonen's Adaptive Subspace Self-Organizing Map (ASSOM) learns several subspaces of the data where each subspace represents some invariant characteristics of the data. To deal with the imbalance classification problem, earlier we have proposed a method for oversampling the minority class using Kohonen's ASSOM. This investigation extends that study, clarifies some issues related to our earlier work, provides the algorithm for generation of the oversamples, applies the method on several benchmark data sets, and makes an application to a Brain Computer Interface (BCI) problem. First we compare the performance of our method using some benchmark data sets with several state-of-The-Art methods. Finally, we apply the ASSOM-based technique to analyze a BCI based application using electroencephalogram (EEG) datasets. Our results demonstrate the effectiveness of the ASSOM-based method in dealing with imbalance classification problem

Extracting Predictor Variables to Construct Breast Cancer Survivability Model with Class Imbalance Problem

Application of data mining methods as a decision support system has a great benefit to predict survival of new patients. It also has a great potential for health researchers to investigate the relationship between risk factors and cancer survival. But due to the imbalanced nature of datasets associated with breast cancer survival, the accuracy of survival prognosis models is a challenging issue for researchers. This study aims to develop a predictive model for 5-year survivability of breast cancer patients and discover relationships between certain predictive variables and survival. The dataset was obtained from SEER database. First, the effectiveness of two synthetic oversampling methods Borderline SMOTE and Density based Synthetic Oversampling method (DSO) is investigated to solve the class imbalance problem. Then a combination of particle swarm optimization (PSO) and Correlation-based feature selection (CFS) is used to identify most important predictive variables. Finally, in order to build a predictive model three classifiers decision tree (C4.5), Bayesian Network, and Logistic Regression are applied to the cleaned dataset. Some assessment metrics such as accuracy, sensitivity, specificity, and G-mean are used to evaluate the performance of the proposed hybrid approach. Also, the area under ROC curve (AUC) is used to evaluate performance of feature selection method. Results show that among all combinations, DSO + PSO_CFS + C4.5 presents the best efficiency in criteria of accuracy, sensitivity, G-mean and AUC with values of 94.33%, 0.930, 0.939 and 0.939, respectively