Rotation Forest Model Research Articles

Predicting gully formation: An approach for assessing susceptibility and future risk

AbstractGully erosion is a significant natural hazard and a form of soil erosion. This research aims to predict gully formation in the Kalshour basin, Sabzevar, Iran. Employing the Information Gain Ratio (IGR) index, we identified 13 key factors out of 22 for modeling, with elevation emerging as the most influential factor in gully formation. The study evaluated the performance of individual machine learning algorithms and ensemble algorithms, including the Functional Tree (FT) as the main classifier, Bagging (Bagg), AdaBoost (Ada), Rotation Forest (RoF), and Random Subspace (RSS). Using a data set of 400 gully and non‐gully points obtained through field investigations (70% for training and 30% for testing), the RoF model achieved an area under the curev (AUC) value of 0.99, indicating its high predictive ability for gully‐susceptible areas. Other algorithms also performed well (Ada: 0.90, FT: 0.92, RSS: 0.94, Bagg: 0.95). However, the RoF algorithm with the functional tree as the main classifier (RoF_FT) demonstrated the highest ability in gully classification and susceptibility mapping, enhancing the functional tree's performance. In addition to AUC, the RoF_FT model achieved an F1 score of 0.89 and an MCC of 0.78 on the validation set, indicating a high balance between precision and recall, and a strong correlation between predicted and actual classes, respectively. Similarly, other models showed robust performance with high F1 scores and MCC values, but the RoF_FT model consistently outperformed them, underscoring its robustness and reliability. The resulting gully erosion‐susceptibility map can be valuable for decision‐makers and local managers in soil conservation and minimizing damages. Implementing proactive measures based on these findings can contribute to sustainable land management practices in the Kalshour basin.Recommendations Gully erosion threat: Gully erosion poses a significant threat to soil, with far‐reaching environmental consequences. Predictive modeling: This research focuses on predicting gully formation in the Kalshour basin, Sabzevar, Iran, using advanced machine learning algorithms. Key findings for decision‐makers: The study evaluates the performance of various machine learning algorithms and ensemble algorithms, with the Functional Tree serving as the main classifier. This not only enhances our ability to predict gully formation but also provides a valuable tool for decision‐makers and local managers in soil conservation. Impact on sustainable land management: By offering a gully erosion‐susceptibility map, the research empowers decision‐makers to implement proactive measures, minimizing damage and contributing to sustainable land management practices. Interdisciplinary approach: The study's combination of geospatial analysis, machine learning, and soil conservation aligns with the journal's mission to advance understanding in environmental modeling.

Environmental and Genetic Interaction Models for Predicting Lung Cancer Risk Using Machine Learning: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis examined environmental and genetic interaction models for predicting lung cancer risk using machine learning techniques. The findings underscore the importance of considering both genetic and environmental factors to enhance predictive accuracy, with significant clinical implications. Among the models reviewed, the Rotation Forest model demonstrated superior performance, achieving an AUC of 0.993, reflecting excellent predictive capabilities. The mean AUC across all models was approximately 0.789, indicating moderate to good discrimination. These results hold promising implications for personalized medicine and clinical decision-making, potentially improving patient outcomes and reducing the global burden of lung cancer. The meta-analysis further highlighted strong performance, with an average TRI-performance (accuracy, precision, recall) of 88.1%, demonstrating robust predictive abilities. Integrating machine learning with multidimensional data deepens the understanding of the biological mechanisms underlying lung cancer and supports its application in precision oncology, paving the way for individualized interventions and improved clinical management.

RFEM: A framework for essential microRNA identification in mice based on rotation forest and multiple feature fusion

With the increasing number of microRNAs (miRNAs), identifying essential miRNAs has become an important task that needs to be solved urgently. However, there are few computational methods for essential miRNA identification. Here, we proposed a novel framework called Rotation Forest for Essential MicroRNA identification (RFEM) to predict the essentiality of miRNAs in mice. We first constructed 1,264 miRNA features of all miRNA samples by fusing 38 miRNA features obtained from the PESM paper and 1,226 miRNA functional features calculated based on miRNA-target gene interactions. Then, we employed 182 training samples with 1,264 features to train the rotation forest model, which was applied to compute the essentiality scores of the candidate samples. The main innovations of RFEM were as follows: 1) miRNA functional features were introduced to enrich the diversity of miRNA features; 2) the rotation forest model used decision tree as the base classifier and could increase the difference among base classifiers through feature transformation to achieve better ensemble results. Experimental results show that RFEM significantly outperformed two previous models with the AUC (AUPR) of 0.942 (0.944) in three comparison experiments under 5-fold cross validation, which proved the model's reliable performance. Moreover, ablation study was further conducted to demonstrate the effectiveness of the novel miRNA functional features. Additionally, in the case studies of assessing the essentiality of unlabeled miRNAs, experimental literature confirmed that 7 of the top 10 predicted miRNAs have crucial biological functions in mice. Therefore, RFEM would be a reliable tool for identifying essential miRNAs.

Comparing Rotation Forest Model And Enhanced Random Forest Model On Imbalanced Data (Application To Classification Of Poverty Households In Sampang Regency, 2019)

<p class="Els-Abstract-text"><em>The first priority of the SDGs is poverty eradication (no poverty). In the Indonesian context, poverty cases has a high correlation with the profession in agriculture sector. For example, in the 2019 Susenas data in Sampang Regency, more than 80% of the sample households categorized as poor have a household head who works in agriculture. Poverty alleviation efforts always begin with identifying/classifying poverty households/families. However, data on poor households is usually unbalanced data that requires special handling in its analysis. This study uses a classification models that are widely used today (in data science world), namely Random Forest and its development methods (Rotation Forest, and Enhanced Random Forest), in classifying poor and non-poor households. The results showed that the forest-based model studied had a low estimation ability when used in cases of unbalanced data, so an approach such as resampling technique was needed before carrying out the classification process. This study cannot conclude which one of the forest model is the most robust for unbalanced data or which method is the most suitable for the use of resampling techniques, but the results of the study show that the use of resampling techniques will improve the quality of the estimation results, especially on sensitivity side.</em><em></em></p><p><em> </em></p><p align="center"> </p>

Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models

Drought is one of the major barriers to the socio-economic development of a region. To manage and reduce the impact of drought, drought vulnerability modelling is important. The use of an ensemble machine learning technique i.e. M5P, M5P -Dagging, M5P-Random SubSpace (RSS) and M5P-rotation forest (RTF) to assess the drought vulnerability maps (DVMs) for the state of Odisha in India was proposed for the first time. A total of 248 drought-prone villages (samples) and 53 drought vulnerability indicators (DVIs) under exposure (28), sensitivity (15) and adaptive capacity (10) were used to produce the DVMs. Out of the total samples, 70% were used for training the models and 30% were used for validating the models. Finally, the DVMs were authenticated by the area under curve (AUC) of receiver operating characteristics, precision, mean-absolute-error, root-mean-square-error, K-index and Friedman and Wilcoxon rank test. Nearly 37.9% of the research region exhibited a very high to high vulnerability to drought. All the models had the capability to model the drought vulnerability. As per the Friedman and Wilcoxon rank test, significant differences occurred among the output of the ensemble models. The accuracy of the M5P base classifier improved after ensemble with RSS and RTF meta classifiers but reduced with Dagging. According to the validation statistics, M5P-RFT model achieved the highest accuracy in modelling the drought vulnerability with an AUC of 0.901. The prepared model would help planners and decision-makers to formulate strategies for reducing the damage of drought.

Four Severity Levels for Grading the Tortuosity of a Retinal Fundus Image.

Hypertensive retinopathy severity classification is proportionally related to tortuosity severity grading. No tortuosity severity scale enables a computer-aided system to classify the tortuosity severity of a retinal image. This work aimed to introduce a machine learning model that can identify the severity of a retinal image automatically and hence contribute to developing a hypertensive retinopathy or diabetic retinopathy automated grading system. First, the tortuosity is quantified using fourteen tortuosity measurement formulas for the retinal images of the AV-Classification dataset to create the tortuosity feature set. Secondly, a manual labeling is performed and reviewed by two ophthalmologists to construct a tortuosity severity ground truth grading for each image in the AV classification dataset. Finally, the feature set is used to train and validate the machine learning models (J48 decision tree, ensemble rotation forest, and distributed random forest). The best performance learned model is used as the tortuosity severity classifier to identify the tortuosity severity (normal, mild, moderate, and severe) for any given retinal image. The distributed random forest model has reported the highest accuracy (99.4%) compared to the J48 Decision tree model and the rotation forest model with minimal least root mean square error (0.0000192) and the least mean average error (0.0000182). The proposed tortuosity severity grading matched the ophthalmologist’s judgment. Moreover, detecting the tortuosity severity of the retinal vessels’, optimizing vessel segmentation, the vessel segment extraction, and the created feature set have increased the accuracy of the automatic tortuosity severity detection model.

Predicting lying, sitting, walking and running using Apple Watch and Fitbit data

ObjectivesThis study’s objective was to examine whether commercial wearable devices could accurately predict lying, sitting and varying intensities of walking and running.MethodsWe recruited a convenience sample of 49 participants (23...

Rotation Forest model modification within the email spam classification

Increased use of email in daily transactions for many businesses or general communication due to its cost-effectiveness has made emails vulnerable to attacks, including spam. Spam emails are unsolicited messages that are very similar to each other and sent to multiple recipients randomly. This study analyzes the Rotation Forest model and modifies it for spam classification problem. Also, the aim of this study is to create a better classifier. To improve classifier stability, the experiments were carried out on Enron spam, Ling spam, and SpamAssasin datasets and evaluated for accuracy, f-measure, precision, and recall.

Using the rotation and random forest models of ensemble learning to predict landslide susceptibility

Ensemble learning methods can be used to evaluate landslide susceptibility when combined with remote sensing (RS) and geographic information systems (GIS). In this study, the rotation forest (ROF) and random forest (RF) ensemble learning models were applied to evaluate landslide susceptibility. The experiments selected the factors by analysing the linear relationship between the factors, explored the optimal proportions of non-landslide samples and landslide samples based on an unbalanced sample dataset, and used the factors before and after the selection to generate landslide susceptibility maps (LSMs) in the Zigui-Badong area. The results show that a suitable ratio between the sample types in the training set can achieve good results for both sensitivity and specificity. The RF models of the study area with 21 factors and 16 factors had sensitivities of 94.22% and 93.59%, respectively. The ROF models with 21 factors and 16 factors had sensitivities of 90.63% and 88.84%, respectively. Although both the RF and ROF models exhibited high accuracy, the RF model achieved a more reasonable and accurate LSM.

Performance Evaluation of the GIS-Based Data-Mining Techniques Decision Tree, Random Forest, and Rotation Forest for Landslide Susceptibility Modeling

This study analyzed and compared landslide susceptibility models using decision tree (DT), random forest (RF), and rotation forest (RoF) algorithms at Woomyeon Mountain, South Korea. Out of a total of 145 landslide locations, 102 locations (70%) were used for model training, and the remaining 43 locations (30%) were used for validation. Fourteen landslide conditioning factors were identified, and the contributions of each factor were evaluated using the RRelief-F algorithm with a 10-fold cross-validation approach. Three factors, timber diameter, age, and density had no contribution to landslide occurrence. Landslide susceptibility maps (LSMs) were produced using DT, RF, and RoF models with the 11 remaining landslide conditioning factors: altitude, slope, aspect, profile curvature, plan curvature, topographic position index, elevation-relief ratio, slope length and slope steepness, topographic wetness index, stream power index, and timber type. The performances of the LSMs were assessed and compared based on sensitivity, specificity, precision, accuracy, kappa index, and receiver operating characteristic curves. The results showed that the ensemble learning methods outperformed the single classifier (DT) and that the RoF model had the highest prediction capability compared to the DT and RF models. The results of this study may be helpful in managing areas vulnerable to landslides and establishing mitigation strategies.

Application of rotation forest with decision trees as base classifier and a novel ensemble model in spatial modeling of groundwater potential.

Groundwater resources are facing a high pressure due to drought and overexploitation. The main aim of this research is to apply rotation forest (RTF) with decision trees as base classifiers and an improved ensemble methodology based on evidential belief function and tree-based models (EBFTM) for preparing groundwater potential maps (GPM). The performance of these new models is then compared with three previously implemented models, i.e., boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF). For this purpose, spring locations in the Meshgin Shahr in Iran were detected. The spring locations were randomly categorized into training (70% of the locations) and validation (30% of the locations) datasets. Furthermore, several groundwater conditioning factors (GCFs) such as hydrogeological, topographical, and land use factors were mapped and regarded as input variables. The tree-based algorithms (i.e., BRT, CART, RF, and RTF) were applied by implementing the input variables and training dataset. The groundwater potential values (i.e., spring occurrence probability) obtained by the BRT, CART, RF, and RTF models for all the pixels of the study area were classified into four potential classes and then used as inputs of the EBF model to construct the new ensemble model (i.e., EBFTM). At last, this paper implemented a receiver operating characteristics (ROC) curve for determining the efficiency of the EBFTM, RTF, BRT, CART, and RF methods. The findings illustrated that the EBFTM had the highest efficacy with an area under the ROC curve (AUC) of 90.4%, followed by the RF, BRT, CART, and RTF models with AUC-ROC values of 90.1, 89.8, 86.9, and 86.2%, respectively. Thus, it could be inferred that the ensemble approach is capable of improving the efficacy of the single tree-based models in GPM production.

Penanganan Data Tidak Seimbang pada Pemodelan Rotation Forest Keberhasilan Studi Mahasiswa Program Magister IPB

Graduate school of Bogor Agricultural University (SPs-IPB) stated that not all students of IPB master program successfully complete their studies. This becomes an evaluation for IPB to be more selective in choosing students in the future. This study aims to model the success classification of IPB master students in 2011 to 2015. The classification method used is rotation forest. The percentage of students who graduated is very large compared to those who did not pass, this can cause the evaluation value different. SMOTE (Synthetic Minority Oversampling Technique) is one of method to handle such unbalanced data by generating artificial data. The ROC (Receiver Operating Characteristic) curve is built to see the optimum cut off value. There are two classification models, they are rotation forest models before and after handled by SMOTE. The comparison results show that the rotation forest model after SMOTE with cut off value 0.6 is the best model. This model can increase the sensitivity value more than 50% although the accuracy and specificity value decreased compared to the modeling before SMOTE.

Classification and diagnosis of cervical cancer with stacked autoencoder and softmax classification

Cervical cancer is one of the most common cancer types in the world, which causes many people to lose their lives. Cancer research is of importance since early diagnosis of cancer facilitates clinical applications for patients. The aim of the study is the automatic diagnosis of cervical cancer. For this purpose, a data set containing 668 samples, 30 attributes and 4 target variables (Schiller, Citology, Biopsy and Hinselmann) from the UCI database was used in the training and testing phases. Softmax classification with stacked autoencoder, one of the deep learning methods, has been used to classify data sets. At first, by applying stacked autoencoder to the raw data set, a reduced dimension dataset is obtained. This data set has been trained for classification by applying the softmax layer. In this phase, 70% (468) of the data set was allocated for training and the remaining 30% (200) for testing. In order to compare the classification performance of the softmax classification with stacked autoencoder, decision tree, kNN, SVM, Feed Forward NN, Rotation Forest models, which are machine learning methods, are used. In the study, proposed models were applied separately to 4 target variables of the dataset, and their classification successes were compared. Finally, softmax classification with stacked autoencoder model, which was applied for the first time in the cervical cancer dataset, performed better than the other machine learning methods with a correct classification rate of 97.8%. Given the interest in machine learning methods in cancer research, new methods of diagnosis are presented in this study in terms of patient diagnostic support systems.

Mapping flood susceptibility in an arid region of southern Iraq using ensemble machine learning classifiers: a comparative study

This study examined the efficacy of three machine ensemble classifiers, namely, random forest, rotation forest and AdaBoost, in assessing flood susceptibility in an arid region of southern Iraq. A dataset was created from flooded and non-flooded areas to train and validate the ensemble classifiers using a binary classification scheme (1—flood, 0—non-flood). The prepared dataset was then partitioned into two sets with a 70/30 ratio: 70% (2478 pixels) for training and 30% (1062 pixels) for testing. A total of 10 influential flood factors were selected and prepared based on data availability and a literature review. The selected factors were surface elevation, slope, plain curvature, topographic wetness index, stream power index, distance to rivers, drainage density, lithology, soil and land use/land cover. The information gain ratio was first utilised to explore the predictive abilities of the factors. The predictive performances of the three ensemble models were compared using six statistical measures: sensitivity, specificity, accuracy, kappa, root mean square error and area under the operating characteristics curve. The results revealed that the AdaBoost classifier was the best in terms of the statistical measures, followed by the random forest and rotation forest models. A flood susceptibility map was prepared based on the result of each classifier and classified into five zones: very low, low, moderate, high and very high. For the model with the best performance, i.e., the AdaBoost model, these zones were distributed over an area of 6002 km2 (44%) for the very low–low zone, 2477 km2 (18%) for the moderate zone and 5048 km2 (40%) for the high–very high zones. This study proved the high capabilities of ensemble machine learning classifiers to decipher flood susceptibility zones in an arid region.

Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)

Landslides are a manifestation of slope instability causing different kinds of damage affecting life and property. Therefore, high-performance-based landslide prediction models are useful to government institutions for developing strategies for landslide hazard prevention and mitigation. Development of data mining based algorithms shows that high-performance models can be obtained using ensemble frameworks. The primary objective of this study is to investigate and compare the use of current state-of-the-art ensemble techniques, such as AdaBoost, Bagging, and Rotation Forest, for landslide susceptibility assessment with the base classifier of J48 Decision Tree (JDT). The Guangchang district (Jiangxi province, China) was selected as the case study. Firstly, a landslide inventory map with 237 landslide locations was constructed; the landslide locations were then randomly divided into a ratio of 70/30 for the training and validating models. Secondly, fifteen landslide conditioning factors were prepared, such as slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), plan curvature, profile curvature, lithology, distance to faults, distance to rivers, distance to roads, land use, normalized difference vegetation index (NDVI), and rainfall. Relief-F with the 10-fold cross-validation method was applied to quantify the predictive ability of the conditioning factors and for feature selection. Using the JDT and its three ensemble techniques, a total of four landslide susceptibility models were constructed. Finally, the overall performance of the resulting models was assessed and compared using area under the receiver operating characteristic (ROC) curve (AUC) and statistical indexes. The result showed that all landslide models have high performance (AUC>0.8). However, the JDT with the Rotation Forest model presents the highest prediction capability (AUC=0.855), followed by the JDT with the AdaBoost (0.850), the Bagging (0.839), and the JDT (0.814), respectively. Therefore, the result demonstrates that the JDT with Rotation Forest is the best optimized model in this study and it can be considered as a promising method for landslide susceptibility mapping in similar cases for better accuracy.

DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model

The Android platform is becoming increasingly popular and various organizations have developed a variety of applications (App) to cater to market trends. Due to the characteristics of the Android platform, such as supporting the unofficial App stores, open source policy and the great tolerance for App verification, it is inevitable that it faces serious problems of malicious software intrusion. In order to protect the users from the serious damages caused by Android malware, we propose a low-cost and high-efficient method to extract permissions, sensitive APIs, monitoring system events and permission-rate as key features, and employ the ensemble Rotation Forest (RF) to construct a model to detect whether an Android App is malicious or not. Specifically, a dataset containing 2,130 samples is used to verify the performance of the proposed method. The experimental results show that the proposed method achieves an high accuracy of 88.26% with 88.40% sensitivity at the precision of 88.16%. To further evaluate the performance of the proposed model, we also compare it with the state-of-the-art Support Vector Machine (SVM) model under the same experimental conditions, and the comparison results demonstrate that the proposed method improves the accuracy by 3.33% compared to SVM. The experimental results show that the proposed model is extremely promising and could provide a cost-effective alternative for Android malware detection.

Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier

Protein-Protein Interactions (PPIs) are essential to most biological processes and play a critical role in most cellular functions. With the development of high-throughput biological techniques and in silico methods, a large number of PPI data have been generated for various organisms, but many problems remain unsolved. These factors promoted the development of the in silico methods based on machine learning to predict PPIs. In this study, we propose a novel method by combining ensemble Rotation Forest (RF) classifier and Discrete Cosine Transform (DCT) algorithm to predict the interactions among proteins. Specifically, the protein amino acids sequence is transformed into Position-Specific Scoring Matrix (PSSM) containing biological evolution information, and then the feature vector is extracted to present protein evolutionary information using DCT algorithm; finally, the ensemble rotation forest model is used to predict whether a given protein pair is interacting or not. When performed on Yeast and H. pylori data sets, the proposed method achieved excellent results with an average accuracy of 98.54% and 88.27%. In addition, we achieved good prediction accuracy of 98.08%, 92.75%, 98.87% and 98.72% on independent data sets (C.elegans, E.coli, H.sapiens and M.musculus). In order to further evaluate the performance of our method, we compare it with the state-of-the-art Support Vector Machine (SVM) classifier and get good results. As a web server, the source code and Yeast data sets used in this article are freely available at http://202.119.201.126:8888/DCTRF/.

Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS

The main objective of this study is to evaluate and compare the performance of landslide models using machine learning ensemble technique for landslide susceptibility assessment. This technique is a combination of ensemble methods (AdaBoost, Bagging, Dagging, MultiBoost, Rotation Forest, and Random SubSpace) and the base classifier of Multiple Perceptron Neural Networks (MLP Neural Nets). Ensemble techniques have been widely applied in other fields; however, their application is still rare in the assessment of landslide problems. Meanwhile, MLP Neural Nets, which is known as an artificial neural network, has been applied widely and efficiently in landslide problems. In the present study, landslide models of part Himalayan area (India) have been constructed and validated. For the evaluation and comparison of these models, receiver operating characteristic curve and Chi Square test methods have been applied. Overall, all landslide models performed well in landslide susuceptibility assessment but the performance of the MultiBoost model is the highest (AUC=0.886), followed by Dagging model (AUC=0.885), the Rotation Forest model (AUC=0.882), the Bagging and Random SubSpace models (AUC=0.881), and the AdaBoost model (AUC=0.876), respectively. Moreover, machine learning ensemble models have improved significantly the performance of the base classifier of MLP Neural Nets (AUC=0.874). Analysis of results indicates that landslide models using machine learning ensemble frameworks are promising methods which can be used as alternatives of individual base classifiers for landslide susceptibility assessment of other prone areas.

Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM.

Protein-Protein Interactions (PPIs) play vital roles in most biological activities. Although the development of high-throughput biological technologies has generated considerable PPI data for various organisms, many problems are still far from being solved. A number of computational methods based on machine learning have been developed to facilitate the identification of novel PPIs. In this study, a novel predictor was designed using the Rotation Forest (RF) algorithm combined with Autocovariance (AC) features extracted from the Position-Specific Scoring Matrix (PSSM). More specifically, the PSSMs are generated using the information of protein amino acids sequence. Then, an effective sequence-based features representation, Autocovariance, is employed to extract features from PSSMs. Finally, the RF model is used as a classifier to distinguish between the interacting and noninteracting protein pairs. The proposed method achieves promising prediction performance when performed on the PPIs of Yeast, H. pylori, and independent datasets. The good results show that the proposed model is suitable for PPIs prediction and could also provide a useful supplementary tool for solving other bioinformatics problems.

Breast cancer diagnosis using GA feature selection and Rotation Forest

Breast cancer is one of the primary causes of death among the women worldwide, and the accurate diagnosis is one of the most significant steps in breast cancer treatment. Data mining techniques can support doctors in diagnosis decision-making process. In this paper, we present different data mining techniques for diagnosis of breast cancer. Two different Wisconsin Breast Cancer datasets have been used to evaluate the system proposed in this study. The proposed system has two stages. In the first stage, in order to eliminate insignificant features, genetic algorithms are used for extraction of informative and significant features. This process reduces the computational complexity and speed up the data mining process. In the second stage, several data mining techniques are employed to make a decision for two different categories of subjects with or without breast cancer. Different individual and multiple classifier systems were used in the second stage in order to construct accurate system for breast cancer classification. The performance of the methods is evaluated using classification accuracy, area under receiver operating characteristic curves and F-measure. Results obtained with the Rotation Forest model with GA-based 14 features show the highest classification accuracy (99.48 %), and when compared with the previous works, the proposed approach reveals the enhancement in performances. Results obtained in this study have potential to open new opportunities in diagnosis of breast cancer.