Use Of Multiple Classifiers Research Articles

Cross-project software defect prediction through multiple learning

Cross-project defect prediction is a method that predicts defects in one software project by using the historical record of another software project. Due to distribution differences and the weak classifier used to build the prediction model, this method has poor prediction performance. Cross-project defect prediction may perform better if distribution differences are reduced, and an appropriate individual classifier is chosen. However, the prediction performance of individual classifiers may be affected in some way by their weaknesses. As a result, in order to boost the accuracy of cross-project defect prediction predictions, this study proposed a strategy that makes use of multiple classifiers and selects attributes that are similar to one another. The proposed method's efficacy was tested using the Relink and AEEEM datasets in an experiment. The findings of the experiments demonstrated that the proposed method produces superior outcomes. To further validate the method, we employed the Wilcoxon sum rank test at 95% significance level. The approach was found to perform significantly better than the baseline methods.

Performance Evaluation of Different Decision Fusion Approaches for Image Classification

Image classification is one of the major data mining tasks in smart city applications. However, deploying classification models that have good generalization accuracy is highly crucial for reliable decision-making in such applications. One of the ways to achieve good generalization accuracy is through the use of multiple classifiers and the fusion of their decisions. This approach is known as “decision fusion”. The requirement for achieving good results with decision fusion is that there should be dissimilarity between the outputs of the classifiers. This paper proposes and evaluates two ways of attaining the aforementioned dissimilarity. One is using dissimilar classifiers with different architectures, and the other is using similar classifiers with similar architectures but trained with different batch sizes. The paper also compares a number of decision fusion strategies.

Deep Learning-based Classification of Fruit Diseases: An Application for Precision Agriculture

Agriculture is essential for the economy and plant disease must be minimized. Early recognition of problems is important, but the manual inspection is slow, error-prone, and has high manpower and time requirements. Artificial intelligence can be used to extract fruit color, shape, or texture data, thus aiding the detection of infections. Recently, the convolutional neural network (CNN) techniques show a massive success for image classification tasks. CNN extracts more detailed features and can work efficiently with large datasets. In this work, we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases. A fine-tuned, pretrained deep learning model (VGG19) was retrained using a plant dataset, from which useful features were extracted. Next, contour features were extracted using pyramid histogram of oriented gradient (PHOG) and combined with the deep features using serial based approach. During the fusion process, a few pieces of redundant information were added in the form of features. Then, a “relevance-based” optimization technique was used to select the best features from the fused vector for the final classifications. With the use of multiple classifiers, an accuracy of up to 99.6% was achieved on the proposed method, which is superior to previous techniques. Moreover, our approach is useful for 5G technology, cloud computing, and the Internet of Things (IoT).

Differentiation of supratentorial singlebrain metastasis and glioblastoma by using peri-enhancing oedema region-derived radiomic features and multiple classifiers.

To differentiate supratentorial single brain metastasis (MET) from glioblastoma (GBM) by using radiomic features derived from the peri-enhancing oedema region and multiple classifiers. One hundred and twenty singlebrain METs and GBMs were retrospectively reviewed and then randomly divided into a training data set (70%) and validation data set (30%). Quantitative radiomic features of each case were extracted from the peri-enhancing oedema region of conventional MR images. After feature selection, five classifiers were built. Additionally, the combined use of the classifiers was studied. Accuracy, sensitivity, and specificity were used to evaluate the classification performance. A total of 321 features were extracted, and 3 features were selected for each case. The 5 classifiers showed an accuracy of 0.70 to 0.76, sensitivity of 0.57 to 0.98, and specificity of 0.43 to 0.93 for the training data set, with an accuracy of 0.56 to 0.64, sensitivity of 0.39 to 0.78, and specificity of 0.50 to 0.89 for the validation data set. When combining the classifiers, the classification performance differed according to the combined mode and the agreement pattern of classifiers, and the greatest benefit was obtained when all the classifiers reached agreement using the same weight and simple majority vote method. Three features derived from the peri-enhancing oedema region had moderate value in differentiating supratentorial single brain MET from GBM with five single classifiers. Combined use of classifiers, like multi-disciplinary team (MDT) consultation, could confer extra benefits, especially for those cases when all classifiers reach agreement. • Radiomics provides a way to differentiate singlebrain MET between GBM by using conventional MR images. • The results of classifiers or algorithms themselves are also data, the transformation of the primary data. • Like MDT consultation, the combined use of multiple classifiers may confer extra benefits.

ENHANCEMENT OF FAST FACE DETECTION ALGORITHM BASED ON A CASCADE OF DECISION TREES

Abstract. Face detection algorithm based on a cascade of ensembles of decision trees (CEDT) is presented. The new approach allows detecting faces other than the front position through the use of multiple classifiers. Each classifier is trained for a specific range of angles of the rotation head. The results showed a high rate of productivity for CEDT on images with standard size. The algorithm increases the area under the ROC-curve of 13% compared to a standard Viola-Jones face detection algorithm. Final realization of given algorithm consist of 5 different cascades for frontal/non-frontal faces. One more thing which we take from the simulation results is a low computational complexity of CEDT algorithm in comparison with standard Viola-Jones approach. This could prove important in the embedded system and mobile device industries because it can reduce the cost of hardware and make battery life longer.

INFFC: An iterative class noise filter based on the fusion of classifiers with noise sensitivity control

In classification, noise may deteriorate the system performance and increase the complexity of the models built. In order to mitigate its consequences, several approaches have been proposed in the literature. Among them, noise filtering, which removes noisy examples from the training data, is one of the most used techniques. This paper proposes a new noise filtering method that combines several filtering strategies in order to increase the accuracy of the classification algorithms used after the filtering process. The filtering is based on the fusion of the predictions of several classifiers used to detect the presence of noise. We translate the idea behind multiple classifier systems, where the information gathered from different models is combined, to noise filtering. In this way, we consider the combination of classifiers instead of using only one to detect noise. Additionally, the proposed method follows an iterative noise filtering scheme that allows us to avoid the usage of detected noisy examples in each new iteration of the filtering process. Finally, we introduce a noisy score to control the filtering sensitivity, in such a way that the amount of noisy examples removed in each iteration can be adapted to the necessities of the practitioner. The first two strategies (use of multiple classifiers and iterative filtering) are used to improve the filtering accuracy, whereas the last one (the noisy score) controls the level of conservation of the filter removing potentially noisy examples. The validity of the proposed method is studied in an exhaustive experimental study. We compare the new filtering method against several state-of-the-art methods to deal with datasets with class noise and study their efficacy in three classifiers with different sensitivity to noise.

Parts-probability-based vehicle detection

Detecting vehicles is important in aerial surveillance. Traditional methods used classifiers to detect vehicles, but a single classifier was limited to detecting vehicles of only one intensity and orientation. Therefore, the task required the use of multiple classifiers of different intensities and orientations. To solve this problem, we first used a latent Dirichlet allocation (LDA) model that improved the previous approaches to vehicle detection. Previous text modeling approaches have been generative. They could be used to build probability models of vehicles in different intensities and from various orientations simultaneously using unlabeled data. Using a probability model, we can detect vehicles in a region with high probability. Next, we used a parts-probability model that improves the LDA model. The model effectively encodes spatial structure among visual words by adding spatial relationships among vehicle parts as priors of words. A parts probability model represents a vehicle hierarchically according to parts appearances and a vehicle’s features within the parts to enforce spatial coherency. Then, we used our model to detect vehicles from a collection of images and demonstrate its performs more effectively.

Feature Selection Inspired Classifier Ensemble Reduction

Classifier ensembles constitute one of the main research directions in machine learning and data mining. The use of multiple classifiers generally allows better predictive performance than that achievable with a single model. Several approaches exist in the literature that provide means to construct and aggregate such ensembles. However, these ensemble systems contain redundant members that, if removed, may further increase group diversity and produce better results. Smaller ensembles also relax the memory and storage requirements, reducing system's run-time overhead while improving overall efficiency. This paper extends the ideas developed for feature selection problems to support classifier ensemble reduction, by transforming ensemble predictions into training samples, and treating classifiers as features. Also, the global heuristic harmony search is used to select a reduced subset of such artificial features, while attempting to maximize the feature subset evaluation. The resulting technique is systematically evaluated using high dimensional and large sized benchmark datasets, showing a superior classification performance against both original, unreduced ensembles, and randomly formed subsets.

EigenNail法における複数識別器による指先押下認識の改善とそのメニュー選択インタフェース応用に向けた基礎検討

EigenNail法における複数識別器による指先押下認識の改善とそのメニュー選択インタフェース応用に向けた基礎検討

PersonalTV

This paper presents an approach to build a TV recommendation system called PersonalTV that enables the use of multiple classifiers, each one specialized on selected attributes of detailed program information. For generating adequate recommendations, the system makes use of content filtering and the preferences directly specified by the user within an MPEG-7 profile. By tracking user actions and interpreting their semantics, the system is able to individually weight these actions and dynamically adjusts the process to the user’s evolving preferences. We show how specialized spam fighting methods can successfully be transferred to the area of recommendation systems and adapted accordingly. Being lightweight, these methods are especially applicable in resource-constrained environments such as TV set-top boxes or mobile devices. Moreover, the use of the variance of the beta-distribution as a confidence value for each recommendation is presented.

Improved classification of crystallization images using data fusion and multiple classifiers

Identifying the conditions that will produce diffraction-quality crystals can require very many crystallization experiments. The use of robots has increased the number of experiments performed in most laboratories, while in structural genomics centres tens of thousands of experiments can be produced every day. Reliable automated evaluation of these experiments is becoming increasingly important. A more robust classification is achieved by combining different methods of feature extraction with the use of multiple classifiers.

Plurality voting-based multiple classifier systems: statistically independent with respect to dependent classifier sets

The simultaneous use of multiple classifiers has been shown to provide performance improvement in classification problems. The selection of an optimal set of classifiers is an important part of multiple classifier systems and the independence of classifier outputs is generally considered to be an advantage for obtaining better multiple classifier systems. In this paper, the need for the classifier independence is interrogated from classification performance point of view. The performance achieved with the use of classifiers having independent joint distributions is compared to some other classifiers which are defined to have best and worst joint distributions. These distributions are obtained by formulating the combination operation as an optimization problem. The analysis revealed several important observations about classifier selection which are then used to analyze the problem of selecting an additional classifier to be used with the available multiple classifier system.

THE COMBINATION OF MULTIPLE CLASSIFIERS BY A NEURAL NETWORK APPROACH

Due to different writing styles and various kinds of noise, the recognition of handwritten numerals is an extremely complicated problem. Recently, a new trend has emerged to tackle this problem by the use of multiple classifiers. This method combines individual classification decisions to derive the final decisions. This is called "Combination of Multiple Classifiers" (CME). In this paper, a novel approach to CME is developed and discussed in detail. It contains two steps: data transformation and data classification. In data transformation, the output values of each classifier are first transformed into a form of likeness measurement. The larger a likeness measurement is, the more probable the corresponding class has the input. In data classification, neural networks have been found very suitable to aggregate the transformed output to produce the final classification decisions. Some strategies for further improving the performance of neural networks have also been proposed in this paper. Experiments with several data transformation functions and data classification approaches have been performed on a large number of handwritten samples. The best result among them is achieved by using both the proposed data transformation function and the multi-layer perceptron neural net, which increased the recognition rate of three individual classifications considerably.