Evolving Ensemble Fuzzy Classifier

The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it addresses the bias and variance dilemma better than its single model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining non-stationary data streams can be found in the literature, most of them are crafted under a static base classifier and revisits preceding samples in the sliding window for a retraining step. This feature causes computationally prohibitive complexity and is not flexible enough to cope with rapidly changing environments. Their complexities are often demanding because it involves a large collection of offline classifiers due to the absence of structural complexities reduction mechanisms and lack of an online feature selection mechanism. A novel evolving ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in this paper. pENsemble differs from existing architectures in the fact that it is built upon an evolving classifier from data streams, termed Parsimonious Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism, which estimates a localized generalization error of a base classifier. A dynamic online feature selection scenario is integrated into the pENsemble. This method allows for dynamic selection and deselection of input features on the fly. pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble structure. The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System

Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification

Pengelasan corak adalah salah satu isu utama dalam pelbagai tugas pencarian data. Dalam kajian ini, fokus penyelidikan tertumpu kepada reka bentuk dan pembinaan model hibrid yang menggabungkan rangkaian neural Teori Resonan Adaptif (ART) terselia dan model Pembelajaran Pengukuhan (RL) untuk pengelasan corak. Secara khususnya, rangkaian ARTMAP Kabur (FAM) dan Pembelajaran-Q dijadikan sebagai tulang belakang dalam merekabentuk dan membina model-model hibrid. Satu model QFAM baharu terlebih dahulu diperkenalkan bagi menambahbaik prestasi pengelasan rangkaian FAM. Strategi pruning dimasukkan bagi mengurangkan kekompleksan QFAM. Bagi mengatasi isu ketidak-telusan, Algoritma Genetik (GA) digunakan bagi mengekstrak hukum kabur if-then daripada QFAM. Model yang terhasil iaitu QFAM-GA, dapat memberi ramalan berserta dengan huraian dengan hanya menggunakan bilangan antisiden yang sedikit. Bagi menambahkan lagi kebolehtahanan model-model Q-FAM, penggunaan sistem agenpelbagai telah dicadangkan. Hasilnya, model gugusan QFAM berasaskan agen dengan ukuran percaya dan kaedah rundingan baharu telah dicadangkan. Pelbagai jenis masalah tanda-aras telah digunakan bagi penilaian model-model gugusan dan individu berasaskan QFAM. Hasilnya telah dianalisa dan dibandingkan dengan FAM serta model-model yang dilaporkan dalam kajian terdahulu. Sebagai tambahan, dua daripada masalah dunia-nyata digunakan bagi menunjukkan kebolehan praktikal model hibrid. Keputusan akhir menunjukkan keberkesanan modul berasaskan QFAM dalam menerajui tugas-tugas pengelasan corak. ________________________________________________________________________________________________________________________ Pattern classification is one of the primary issues in various data mining tasks. In this study, the main research focus is on the design and development of hybrid models, combining the supervised Adaptive Resonance Theory (ART) neural network and Reinforcement Learning (RL) models for pattern classification. Specifically, the Fuzzy ARTMAP (FAM) network and Q-learning are adopted as the backbone for designing and developing the hybrid models. A new QFAM model is first introduced to improve the classification performance of FAM network. A pruning strategy is incorporated to reduce the complexity of QFAM. To overcome the opaqueness issue, a Genetic Algorithm (GA) is used to extract fuzzy if-then rules from QFAM. The resulting model, i.e. QFAM-GA, is able to provide predictions with explanations using only a few antecedents. To further improve the robustness of QFAM-based models, the notion of multi agent systems is employed. As a result, an agent-based QFAM ensemble model with a new trust measurement and negotiation method is proposed. A variety of benchmark problems are used for evaluation of individual and ensemble QFAM-based models. The results are analyzed and compared with those from FAM as well as other models reported in the literature. In addition, two real-world problems are used to demonstrate the practicality of the hybrid models. The outcomes indicate the effectiveness of QFAM-based models in tackling pattern classification tasks

Review of Face Detection Systems Based Artificial Neural Networks Algorithms

Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa