An improved rotation forest algorithm based on heterogeneous classifiers ensemble for classifying gene expression profile

Chen, Tao

doi:10.18280/ama_b.600101

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…In [14], a rotation forest algorithm created on heterogeneous classifiers ensemble is applied to classified the gene expression outline. The local optimum and overfitting were improved through heterogeneous rotation forests.…”

Section: Related Workmentioning

confidence: 99%

A combined approach of base and meta learners for hybrid system

Abro

SIDDIQUE

Talpur

et al. 2023

Turkish Journal of Engineering

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The ensemble learning method is considered a meaningful yet challenging task. To enhance the performance of binary classification and predictive analysis, this paper proposes an effective ensemble learning approach by applying multiple models to produce efficient and effective outcomes. In these experimental studies, three base learners, J48, Multilayer Perceptron (MP), and Support Vector Machine (SVM) are being utilized. Moreover, two meta-learners, Bagging and Rotation Forest are being used in this analysis. Firstly, to produce effective results and capture productive data, the base learner, the J48 decision tree is aggregated with the rotation forest. Secondly, machine learning and ensemble learning classification algorithms along with the five UCI Datasets are being applied to progress the robustness of the system. Whereas, the recommended mechanism is evaluated by implementing five performance standards concerning the accuracy, AUC (Area Under Curve), precision, recall and F-measure values. In this regard, extensive strategies and various approaches were being studied and applied to obtain improved results from the current literature; however, they were insufficient to provide successful results. We present experimental results which demonstrate the efficiency of our approach to well-known competitive approaches. This method can be applied to image identification and machine learning problems, such as binary classification.

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Section: Related Workmentioning

confidence: 99%

A combined approach of base and meta learners for hybrid system

Abro

SIDDIQUE

Talpur

et al. 2023

Turkish Journal of Engineering

View full text Add to dashboard Cite

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“…Another algorithm used is Rotation Forest [23] with base classifier C4.5 decision tree. The experiments were conducted in the feature set of 800 features and the best performance of the model, for every 10 iterations of the Rotation Forest algorithm, up to 100 iterations, is presented in Table 14.…”

Section: Improving the Accuracymentioning

confidence: 99%

Age and Gender as Cyber Attribution Features in Keystroke Dynamic-Based User Classification Processes

2021

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Keystroke dynamics are used to authenticate users, to reveal some of their inherent or acquired characteristics and to assess their mental and physical states. The most common features utilized are the time intervals that the keys remain pressed and the time intervals that are required to use two consecutive keys. This paper examines which of these features are the most important and how utilization of these features can lead to better classification results. To achieve this, an existing dataset consisting of 387 logfiles is used, five classifiers are exploited and users are classified by gender and age. The results, while demonstrating the application of these two characteristics jointly on classifiers with high accuracy, answer the question of which keystroke dynamics features are more appropriate for classification with common classifiers.

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“…Extensive exploitation of the RotF as a base learner can take place during both the process of discerning the most questionable unlabeled instances, so as to get labeled by a human expert, and the building of the intermediate classification models during the conducted iterations along with the final one. By this procedure, the proposed algorithm may sufficiently benefited concerning the properties of stability and robustness, either acting individually or as a wrapper technique based on heterogeneous base learners . Consequently, by obtaining decent disambiguating performance over the exploitation of unlabeled pool, a dramatically large reduction of the human effort spent is achieved, acquiring satisfactory quality of classification accuracy rates through a small number of iterations.…”

Section: Introductionmentioning

confidence: 99%

Active learning Rotation Forest for multiclass classification

Kazllarof

Karlos

Kotsiantis

2019

Computational Intelligence

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Although the achievements of the computer science field have facilitated the tasks of collecting, storing, and accessing vast amounts of data efficiently, its annotation still remains a non–easily resolvable problem since no automated mechanism can perform reliably enough. This fact is even more profound when the objective is the high quality of generalization ability. Active learning constitutes a scheme that is exploited for tackling such problems, controlling the demanded human effort under a trade‐off assumption concerning the achievement of higher accuracy rates. In this work, the well‐known Rotation Forest algorithm is integrated with the active learning theory for constructing a robust and accurate classifier. Thus, apart from exploiting the collected labeled data, unlabeled data mining takes place through appropriate queries, whereas human expert decisions over the most questionable of them enrich the initial ones. Comprehensive comparisons of the proposed algorithm against four distinct learners inside the same learning scheme were executed. Moreover, the baseline strategy of random sampling and the corresponding supervised scenarios were included. During the evaluation stage, 13 publicly available multiclass datasets were assessed, and the obtained results verified our assumptions, regarding also the significant supremacy of the proposed algorithm against the majority of its rivals.

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An improved rotation forest algorithm based on heterogeneous classifiers ensemble for classifying gene expression profile

Cited by 5 publications

References 30 publications

A combined approach of base and meta learners for hybrid system

A combined approach of base and meta learners for hybrid system

Age and Gender as Cyber Attribution Features in Keystroke Dynamic-Based User Classification Processes

Active learning Rotation Forest for multiclass classification

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