Practical Classification Applications Research Articles

An Undersampling Method Approaching the Ideal Classification Boundary for Imbalance Problems

Data imbalance is a common problem in most practical classification applications of machine learning, and it may lead to classification results that are biased towards the majority class if not dealt with properly. An effective means of solving this problem is undersampling in the borderline area; however, it is difficult to find the area that fits the classification boundary. In this paper, we present a novel undersampling framework, whereby the clustering of samples in the majority class is conducted and segmentation is then performed in the boundary area according to the clusters obtained; this enables a better shape that fits the classification boundary to be obtained via the performance of random sampling in the borderline area of these segments. In addition, we hypothesize that there exists an optimal number of classifiers to be integrated into the method of ensemble learning that utilizes multiple classifiers that have been obtained via sampling to promote the algorithm. After passing the hypothesis test, we apply the improved algorithm to the newly developed method. The experimental results show that the proposed method works well.

Algorithm selection using edge ML and case-based reasoning

In practical data mining, a wide range of classification algorithms is employed for prediction tasks. However, selecting the best algorithm poses a challenging task for machine learning practitioners and experts, primarily due to the inherent variability in the characteristics of classification problems, referred to as datasets, and the unpredictable performance of these algorithms. Dataset characteristics are quantified in terms of meta-features, while classifier performance is evaluated using various performance metrics. The assessment of classifiers through empirical methods across multiple classification datasets, while considering multiple performance metrics, presents a computationally expensive and time-consuming obstacle in the pursuit of selecting the optimal algorithm. Furthermore, the scarcity of sufficient training data, denoted by dimensions representing the number of datasets and the feature space described by meta-feature perspectives, adds further complexity to the process of algorithm selection using classical machine learning methods. This research paper presents an integrated framework called eML-CBR that combines edge edge-ML and case-based reasoning methodologies to accurately address the algorithm selection problem. It adapts a multi-level, multi-view case-based reasoning methodology, considering data from diverse feature dimensions and the algorithms from multiple performance aspects, that distributes computations to both cloud edges and centralized nodes. On the edge, the first-level reasoning employs machine learning methods to recommend a family of classification algorithms, while at the second level, it recommends a list of the top-k algorithms within that family. This list is further refined by an algorithm conflict resolver module. The eML-CBR framework offers a suite of contributions, including integrated algorithm selection, multi-view meta-feature extraction, innovative performance criteria, improved algorithm recommendation, data scarcity mitigation through incremental learning, and an open-source CBR module, reshaping research paradigms. The CBR module, trained on 100 datasets and tested with 52 datasets using 9 decision tree algorithms, achieved an accuracy of 94% for correct classifier recommendations within the top k=3 algorithms, making it highly suitable for practical classification applications.

A new classification method based on the negation of a basic probability assignment in the evidence theory

In the practical application of classification, how to handle uncertain information for efficient classification is a hot topic. In this paper, in the frame of Dempster–Shafer evidence theory, a new classification method based on the negation of basic probability assignment (BPA) is proposed to implement an effective classification. The proposed method addresses the issue that the values of samples’ attributes cannot clearly point out a certain class in classification problems. For uncertain information modeling, the negation of BPA is adopted to obtain more valuable information in the body of evidence. To measure the uncertain information represented by the negation of BPA, the belief entropy is used for calculating the uncertain degree of each body of evidence. Finally, Dempster’s combination rule is used for data fusion to identify and recognize the unknown class. The effectiveness and efficiency of the new classification method are validated according to experiments on several UCI data sets. In addition, the classification experiment on the data sets with the changing proportion of the training set verifies that the method is robust and feasible.

Study on the potential of whitening transformation in improving single crop mapping accuracy

In some practical classification applications, our focus may be just a specific crop (e.g., rice, corn, or soybean). Therefore, how to distinguish the crop of interest from other classes accurately and quickly is an issue worth studying. We propose an approach that combines whitening transformation (WT)/interest-class-based whitening transformation (ICWT) and one-class classification (OCC) methods to identify the crop of interest. The image created by applying WT/ICWT to the original image is referred to as the totally/partially whitened image, respectively. Three typical OCC classifiers—one-class support vector machine (OCSVM), positive and unlabeled learning (PUL), and maximum entropy (MaxEnt)—are trained to identify the crop of interest, using a totally/partially whitened image. The research results demonstrate that (1) both ICWT and WT enabled OCSVM to improve the producer’s accuracy of the crop of interest by more than 20%, and the performance of the proposed approach reached or surpassed that of conventional supervised methods; (2) PUL and MaxEnt performed better than OCSVM, especially in identifying local major crops using different phased data; and (3) the proposed approach is efficient in single crop identification with remote sensing imagery and is worth recommending for practical applications.

Ensemble Classification of Data Streams Based on Attribute Reduction and a Sliding Window

With the current increasing volume and dimensionality of data, traditional data classification algorithms are unable to satisfy the demands of practical classification applications of data streams. To deal with noise and concept drift in data streams, we propose an ensemble classification algorithm based on attribute reduction and a sliding window in this paper. Using mutual information, an approximate attribute reduction algorithm based on rough sets is used to reduce data dimensionality and increase the diversity of reduced results in the algorithm. A double-threshold concept drift detection method and a three-stage sliding window control strategy are introduced to improve the performance of the algorithm when dealing with both noise and concept drift. The classification precision is further improved by updating the base classifiers and their nonlinear weights. Experiments on synthetic datasets and actual datasets demonstrate the performance of the algorithm in terms of classification precision, memory use, and time efficiency.

A new feature selection approach based on ensemble methods in semi-supervised classification

In computer aided medical system, many practical classification applications are confronted to the massive multiplication of collection and storage of data, this is especially the case in areas such as the prediction of medical test efficiency, the classification of tumors and the detection of cancers. Data with known class labels (labeled data) can be limited but unlabeled data (with unknown class labels) are more readily available. Semi-supervised learning deals with methods for exploiting the unlabeled data in addition to the labeled data to improve performance on the classification task. In this paper, we consider the problem of using a large amount of unlabeled data to improve the efficiency of feature selection in large dimensional datasets, when only a small set of labeled examples is available. We propose a new semi-supervised feature evaluation method called Optimized co-Forest for Feature Selection (OFFS) that combines ideas from co-forest and the embedded principle of selecting in Random Forest based by the permutation of out-of-bag set. We provide empirical results on several medical and biological benchmark datasets, indicating an overall significant improvement of OFFS compared to four other feature selection approaches using filter, wrapper and embedded manner in semi-supervised learning. Our method proves its ability and effectiveness to select and measure importance to improve the performance of the hypothesis learned with a small amount of labeled samples by exploiting unlabeled samples.

Data fusion based framework for the recognition of Isolated Handwritten Kannada Numerals

combining classifiers appears as a natural step forward when a critical mass of knowledge of single classifier models has been accumulated. Although there are many unanswered questions about matching classifiers to real-life problems, combining classifiers is rapidly growing and enjoying a lot of attention from pattern recognition and machine learning communities. For any pattern classification task, an increase in data size, number of classes, dimension of the feature space and interclass separability affect the performance of any classifier. It is essential to know the effect of the training dataset size on the recognition performance of a feature extraction method and classifier. In this paper, an attempt is made to measure the performance of the classifier by testing the classifier with two different datasets of different sizes. In practical classification applications, if the number of classes and multiple feature sets for pattern samples are given, a desirable recognition performance can be achieved by data fusion. In this paper, we have proposed a framework based on the combined concepts of decision fusion and feature fusion for the isolated handwritten Kannada numerals classification. The proposed method improves the classification result. From the experimental results it is seen that there is an increase of 13.95% in the recognition accuracy.

Algorithms of diagnostics of widespread dysplastic syndromes and phenotypes. Theoretical approaches and practical application of classifi cation

The heritable connective tissue disorders with common general phenotypical and clinical features should be called dysplasia of the connective tissue. Classification of the basic dysplastical syndromes and phenotypes is offered. Article considers the algorithm of diagnostics of the basic syndromes and phenotypes and the practical questions arising at attempt of their classification.