Mahardhika Pratama

PANFIS: A Novel Incremental Learning Machine

Most of the dynamics in real-world systems are compiled by shifts and drifts, which are uneasy to be overcome by omnipresent neuro-fuzzy systems. Nonetheless, learning in nonstationary environment entails a system owning high degree of flexibility capable of assembling its rule base autonomously according to the degree of nonlinearity contained in the system. In practice, the rule growing and pruning are carried out merely benefiting from a small snapshot of the complete training data to truncate the computational load and memory demand to the low level. An exposure of a novel algorithm, namely parsimonious network based on fuzzy inference system (PANFIS), is to this end presented herein. PANFIS can commence its learning process from scratch with an empty rule base. The fuzzy rules can be stitched up and expelled by virtue of statistical contributions of the fuzzy rules and injected datum afterward. Identical fuzzy sets may be alluded and blended to be one fuzzy set as a pursuit of a transparent rule base escalating humans interpretability. The learning and modeling performances of the proposed PANFIS are numerically validated using several benchmark problems from real-world or synthetic datasets. The validation includes comparisons with state-of-the-art evolving neuro-fuzzy methods and showcases that our new method can compete and in some cases even outperform these approaches in terms of predictive fidelity and model complexity.

Generalized smart evolving fuzzy systems

AbstractIn this paper, we propose a new methodology for learning evolving fuzzy systems (EFS) from data streams in terms of on-line regression/system identification problems. It comes with enhanced dynamic complexity reduction steps, acting on model components and on the input structure and by employing generalized fuzzy rules in arbitrarily rotated position. It is thus termed as Gen-Smart-EFS (GS-EFS), short for generalized smart evolving fuzzy systems. Equipped with a new projection concept for high-dimensional kernels onto one-dimensional fuzzy sets, our approach is able to provide equivalent conventional TS fuzzy systems with axis-parallel rules, thus maintaining interpretability when inferring new query samples. The on-line complexity reduction on rule level integrates a new merging concept based on a combined adjacency–homogeneity relation between two clusters (rules). On input structure level, complexity reduction is motivated by a combined statistical-geometric concept and acts in a smooth and soft manner by incrementally adapting feature weights: features may get smoothly out-weighted over time (

pClass: An Effective Classifier for Streaming Examples

\rightarrow

Evolving Type-2 Fuzzy Classifier

→soft on-line dimension reduction) but also may become reactivated at a later stage. Out-weighted features will contribute little to the rule evolution criterion, which prevents the generation of unnecessary rules and reduces over-fitting due to curse of dimensionality. The criterion relies on a newly developed re-scaled Mahalanobis distance measure for assuring monotonicity between feature weights and distance values. Gen-Smart-EFS will be evaluated based on high-dimensional real-world data (streaming) sets and compared with other well-known (evolving) fuzzy systems approaches. The results show improved accuracy with lower rule base complexity as well as smaller rule length when using Gen-Smart-EFS.

Recurrent Classifier Based on an Incremental Metacognitive-Based Scaffolding Algorithm

Nowadays, there is increasing demand for an integrated system usable to real-time environments under limited computational resources and minimum operator supervision. In contrast, the model is also supposed to actualize high predictive quality in order to confirm the process safety and attractive working framework allowing the user to grasp how the particular task is settled. A holistic concept of a fully data-driven modeling tool namely Generic Evolving Neuro-Fuzzy Inference System (GENEFIS) is proposed in this paper. The major spotlight of GENEFIS is in delivering a sensible tradeoff between high predictive accuracy and parsimonious rule base while reckoning tractable rule semantics. The viability of GENEFIS is numerically validated via a series of experimentations using real world and artificial datasets and is compared against state of the art of the evolving neuro-fuzzy systems (ENFSs), where GENEFIS not only showcases higher predictive accuracies but also lands on more frugal structures than other algorithms.

An incremental meta-cognitive-based scaffolding fuzzy neural network

In this paper, a novel evolving fuzzy-rule-based classifier, termed parsimonious classifier (pClass), is proposed. pClass can drive its learning engine from scratch with an empty rule base or initially trained fuzzy models. It adopts an open structure and plug and play concept where automatic knowledge building, rule-based simplification, knowledge recall mechanism, and soft feature reduction can be carried out on the fly with limited expert knowledge and without prior assumptions to underlying data distribution. In this paper, three state-of-the-art classifier architectures engaging multi-input-multi-output, multimodel, and round robin architectures are also critically analyzed. The efficacy of the pClass has been numerically validated by means of real-world and synthetic streaming data, possessing various concept drifts, noisy learning environments, and dynamic class attributes. In addition, comparative studies with prominent algorithms using comprehensive statistical tests have confirmed that the pClass delivers more superior performance in terms of classification rate, number of fuzzy rules, and number of rule-base parameters.

An Incremental Type-2 Meta-Cognitive Extreme Learning Machine

In this paper, a novel fuzzy neural network termed as dynamic parsimonious fuzzy neural network (DPFNN) is proposed. DPFNN is a four layers network, which features coalescence between TSK (Takagi-Sugeno-Kang) fuzzy architecture and multivariate Gaussian kernels as membership functions. The training procedure is characterized by four aspects: (1) DPFNN may evolve fuzzy rules as new training datum arrives, which enables to cope with non-stationary processes. We propose two criteria for rule generation: system error and @e-completeness reflecting both a performance and sample coverage of an existing rule base. (2) Insignificant fuzzy rules observed over time based on their statistical contributions are pruned to truncate the rule base complexity and redundancy. (3) The extended self organizing map (ESOM) theory is employed to dynamically update the centers of the ellipsoidal basis functions in accordance with input training samples. (4) The optimal fuzzy consequent parameters are updated by time localized least square (TLLS) method that exploits a concept of sliding window in order to reduce the computational burden of the least squares (LS) method. The viability of the new method is intensively investigated based on real-world and artificial problems as it is shown that our method not only arguably delivers more compact and parsimonious network structures, but also achieves lower predictive errors than state-of-the-art approaches.

Scaffolding type-2 classifier for incremental learning under concept drifts

Evolving fuzzy classifiers (EFCs) have achieved immense success in dealing with nonstationary data streams because of their flexible characteristics. Nonetheless, most real-world data streams feature highly uncertain characteristics, which cannot be handled by the type-1 EFC. A novel interval type-2 fuzzy classifier, namely evolving type-2 classifier (eT2Class), is proposed in this paper, which constructs an evolving working principle in the framework of interval type-2 fuzzy system. The eT2Class commences its learning process from scratch with an empty or initially trained rule base, and its fuzzy rules can be automatically grown, pruned, recalled, and merged on the fly referring to summarization power and generalization power of data streams. In addition, the eT2Class is driven by a generalized interval type-2 fuzzy rule, where the premise part is composed of the multivariate Gaussian function with an uncertain nondiagonal covariance matrix, while employing a subset of the nonlinear Chebyshev polynomial as the rule consequents. The efficacy of the eT2Class has been rigorously assessed by numerous real-world and artificial study cases, benchmarked against state-of-the-art classifiers, and validated through various statistical tests. Our numerical results demonstrate that the eT2Class produces more reliable classification rates, while retaining more compact and parsimonious rule base than state-of-the-art EFCs recently published in the literature.