SVM-Based Cost-sensitive Classification Algorithm with Error Cost and Class-dependent Reject Cost

In such real data mining applications as medical diagnosis, fraud detection and fault classification, and so on, the two problems that the error cost is expensive and the reject cost is class-dependent are often encountered. In order to overcome those problems, firstly, the general mathematical description of the Binary Classification Problem with Error Cost and Class-dependent Reject Cost (BCP-EC2RC) is proposed. Secondly, as one of implementation methods of BCP-EC2RC, the new algorithm, named as Cost-sensitive Support Vector Machines with the Error Cost and the Class-dependent Reject Cost (CSVM-EC2RC), is presented. The CSVM-EC2RC algorithm involves two stages: estimating the classification reliability based on trained SVM classifier, and determining the optimal reject rate of positive class and negative class by minimizing the average cost based on the given error cost and class-dependent reject cost. The experiment studies based on a benchmark data set illustrate that the proposed algorithm is effective.

The main purpose of traditional classification algorithms on bioinformatics application is to acquire better classification accuracy. However, these algorithms cannot meet the requirement that minimises the average misclassification cost. In this paper, a new algorithm of cost-sensitive regularised extreme learning machine (CS-RELM) was proposed by using probability estimation and misclassification cost to reconstruct the classification results. By improving the classification accuracy of a group of small sample which higher misclassification cost, the new CS-RELM can minimise the classification cost. The 'rejection cost' was integrated into CS-RELM algorithm to further reduce the average misclassification cost. By using Colon Tumour dataset and SRBCT (Small Round Blue Cells Tumour) dataset, CS-RELM was compared with other cost-sensitive algorithms such as extreme learning machine (ELM), cost-sensitive extreme learning machine, regularised extreme learning machine, cost-sensitive support vector machine (SVM). The results of experiments show that CS-RELM with embedded rejection cost could reduce the average cost of misclassification and made more credible classification decision than others.

Passerine hosts of parasitic cuckoos usually vary in their ability to discriminate and reject cuckoo eggs. Costs of discrimination and rejection errors have been invoked to explain the maintenance of this within-population variability. Recently, enforcement of acceptance by parasites has been identified as a rejection cost in the magpie (Pica pica) and its brood parasite, the great spotted cuckoo (Clamator glandarius). Previous experimental work has shown that rejecter magpies suffer from increased nest predation by the great spotted cuckoo. Cuckoo predatory behavior is supposed to confer a selective advantage to the parasite because magpies experiencing a reproductive failure may provide a second opportunity for the cuckoo to parasitize a replacement clutch. This hypothesis implicitly assumes that magpies modulate their propensity to reject parasite eggs as a function of previous experience. We tested this hypothesis in a magpie population breeding in study plots varying in parasitism rate. Magpie pairs that were experimentally parasitized and had their nests depredated, after their rejection behavior had been assessed, changed their behavior from rejection to acceptance. The change in host behavior was prominent in study plots with high levels of parasitism, but not in plots with rare or no cuckoo parasitism. We discuss three possible explanations for these differences, concluding that in study plots with a high density of cuckoos, the probability for a rejecter magpie nest of being revisited and depredated by a cuckoo is high, particularly for replacement clutches, and, therefore, the cost for magpies of rejecting a cuckoo egg in a replacement clutch is increased. Moreover, in areas with high levels of host defense (low parasitism rate), the probability of parasitism and predation of rejecter-magpie nests by the cuckoo is reduced in both first and replacement clutches. Therefore, rejecter magpies in such areas should not change their rejection behavior in replacement clutches.

The application of association rule mining to classification has led to a new family of classifiers which are often referred to as associative classifiers (ACs). An advantage of ACs is that they are rule-based and thus lend themselves to an easier interpretation. Rule-based classifiers can play a very important role in applications such as medical diagnosis and fraud detection where data are the norm and not the exception. The focus of this paper is to extend and modify ACs for classification on imbalanced data sets using only statistical techniques. We combine the use of statistically significant rules with a new measure, the Class Correlation Ratio (CCR), to build an AC which we call SPARCCC. Experiments show that in terms of classification quality, SPARCCC performs comparably on balanced datasets and outperforms other AC techniques on imbalanced data sets. It also has a significantly smaller rule base and is much more computationally efficient.

Capsule endoscopy (CE), introduced as a modality for non-invasive examination of entire gastrointestinal tract, demands for an efficient computer-aided decision making system to relieve the physician from the responsibility of screening around 60,000 video frames per patient. An automatic and robust segmentation algorithm can aid the automation of CE screening and decision making procedure. In this paper, we propose a new segmentation algorithm based on GrowCut and apply the algorithm for CE images containing bleeding. To substitute the manual seed input in traditional GrowCut segmentation, the proposed Automated GrowCut (AGC) algorithm initially segments the images using clustering. The cluster centroids, subsequently labeled as bleeding, non-bleeding and background by a trained SVM classifier, serve as seeds for the GrowCut segmentation. A comprehensive evaluation and comparison with respect to ground truth exhibits that the proposed method can achieve a Dice Similarity Coefficient of 0.81, comparable to the interactive GrowCut, requiring only 13.96% of the computation time of interactive GrowCut. The comparison with two other state-of-the-art unsupervised segmentation methods, unsupervised GrowCut and Fuzzy c-means, further justifies the suitability of the proposed method for automated segmentation and annotation of CE images.

Healthcare insurance fraud is a growing global issue that undermines the integrity of healthcare systems and imposes significant financial losses on governments, insurers, and consumers. Fraudulent activities, including false claims, overbilling, and identity theft, inflate healthcare costs and reduce the efficiency of insurance systems. Traditional fraud detection systems rely on centralized databases and intermediaries, which are prone to inefficiencies, security vulnerabilities, and manipulation. In response to these challenges, this article proposed a decentralized blockchain‐based solution to detect and prevent healthcare insurance fraud more effectively. In healthcare insurance, blockchain can create a trusted, real‐time system for recording insurance claims and transactions. By securely sharing data between healthcare providers, insurance companies, and regulatory bodies, blockchain enables greater transparency and auditability. Smart contracts, self‐executing contracts coded on the blockchain, can automate claim processes, reducing the potential for fraudulent claims by ensuring that all predefined conditions are met before payments are processed. Furthermore, blockchain can track every healthcare and insurance process step, from patient records and treatments to billing and reimbursement, allowing for real‐time detection of irregularities. The proposed decentralized blockchain approach offers a secure and efficient solution for detecting and preventing healthcare insurance fraud. It minimizes reliance on centralized authorities, reduces operational costs, and builds a more transparent and trustworthy system (For 10 patients, the total execution time includes 0.007 ms for hashing, 0.065 ms for encryption, and 0.060 ms for decryption). The proposed system's smart contracts obtained results (Ether cost) show that submitting a claim costs 0.01677963 ETH, flagging a claim costs 0.002234 ETH, approving a claim costs 0.00385737 ETH, and the fraud detection constructor incurs the highest cost of 0.11428872 ETH. By leveraging blockchain's strengths, the healthcare industry can proactively combat fraud, protect sensitive data, and improve overall efficiency.

<p><span>Healthcare fraud has become a common encounter in the healthcare finance industry. The financial security of healthcare payers and providers is seriously impacted by healthcare fraud. When incorrect or exaggerated medical services are invoiced for reimbursement, fraudulent healthcare claims result. The effective operation of the healthcare system depends on the detection of such fraudulent actions. This paper develops a healthcare claim fraud detection method based on ensemble learning. Stack ensemble learning algorithm performance is compared to that of methods such as multi-layer perceptron (MLP) classifier, support vector classifier (SVC), logistic regression (LR), and decision tree (DT) algorithm. Because of the healthcare data imbalance, the normal transaction is significantly higher than the fraudulent transaction. The machine learning (ML) algorithm performs poorly because imbalanced data causes it to be biased toward the majority class. As a result, the data is unsampled using the synthetic minority oversampling technique (SMOTE) technique to provide balanced data. The experimental results show that for the identification of healthcare claim fraud, the ensemble learning strategy greatly <span>outperforms single learning algorithms. The stack ensemble learning outperforms all the area under the curve for the receiver-operating characteristic (AUC ROC) curves from various algorithms, and the AUC-ROC curve is determined to be producing results that are adequate for all approaches.</span></span></p>

Despite efforts to prevent it, fraud in the United States healthcare system remains a serious and pressing issue. Since healthcare fraud is a complex and multi‐faceted problem, fraud‐fighting solutions must be flexible enough to address the ever‐evolving nature of the crime. Here, we present a method to identify healthcare fraud in such a manner that incorporates both potential fraud as well as risky provider behavior. The proposed weighted risk model provides a framework for creating a dynamic fraud detection database that can be easily scaled up to incorporate emerging fraud schemes.

Frequent itemset generation is the prerequisite and most time-consuming process for association rule mining. Nowadays, most efficient apriori-like algorithms rely heavily on the minimum support constraint to prune a vast amount of non-candidate itemsets. This pruning technique, however, becomes less useful for some real applications where the supports of interesting itemsets are extremely small, such as medical diagnosis, fraud detection, among the others. In this paper, we propose a new algorithm that maintains its performance even at relative low supports. Empirical evaluations show that our algorithm is, on the average, more than an order of magnitude faster than a priori-like algorithms.

Data mining examines large pre-existing databases in order to generate new information. There are various tasks included under Data mining and association rule mining is considered as one of the crucial tasks among its. They are in form of if-then kind of statements which help to find relationships among huge data which do not hold relationship with each other within a relational database or any other information repository. As there are many applications like market basket analysis, detection of fraud in web, medical diagnosis, census data, Customer Relationship Management of credit card business which uses association rules so it is possible to improve the process of Decision making. Security is required for individual transaction and for frequent itemsets when the database is partitioned horizontally among multiple sites. In this case, every site is interested in globally supported association rules without revealing its own local information. To fulfill this goal, We use a secure multi-party algorithm based on secure sum technique to simplify the operation of mining association rule when the database is horizontally partitioned among multiple sites. We are using a Frequent-Pattern (FP) growth algorithm to find frequent itemsets and try to reduce total computation time.

Recently, anomaly detection has acquired a realistic response from data mining scientists as a graph of its reputation has increased smoothly in various practical domains like product marketing, fraud detection, medical diagnosis, fault detection and so many other fields. High dimensional data subjected to outlier detection poses exceptional challenges for data mining experts and it is because of natural problems of the curse of dimensionality and resemblance of distant and adjoining points. Traditional algorithms and techniques were experimented on full feature space regarding outlier detection. Customary methodologies concentrate largely on low dimensional data and hence show ineffectiveness while discovering anomalies in a data set comprised of a high number of dimensions. It becomes a very difficult and tiresome job to dig out anomalies present in high dimensional data set when all subsets of projections need to be explored. All data points in high dimensional data behave like similar observations because of its intrinsic feature i.e., the distance between observations approaches to zero as the number of dimensions extends towards infinity. This research work proposes a novel technique that explores deviation among all data points and embeds its findings inside well established density-based techniques. This is a state of art technique as it gives a new breadth of research towards resolving inherent problems of high dimensional data where outliers reside within clusters having different densities. A high dimensional dataset from UCI Machine Learning Repository is chosen to test the proposed technique and then its results are compared with that of density-based techniques to evaluate its efficiency.

<title>Abstract</title> Class imbalance remains a critical challenge in machine learning, particularly in high stakes domains such as fraud detection and medical diagnosis, where minority class instances carry greater significance. Traditional classifiers often exhibit biased performance toward the majority class, leading to suboptimal detection of rare but important events. To address this challenge, this paper proposes an Enhanced Genetic Algorithm based Data Balancer (EGADB) that optimally resamples imbalanced datasets prior to classification. The proposed technique leverages genetic optimization principles to generate a balanced training set while preserving the intrinsic data distribution. The effectiveness of EGADB is evaluated using multiple base learners including Logistic Regression, Naive Bayes, and Bagging, as well as a developed voter based ensemble model. Experiments are conducted on the Credit Card Fraud Detection dataset and a breast cancer dataset, comparing classifier performance on unbalanced data and data balanced using EGADB. Performance is assessed using accuracy, precision, recall, F1 score, optimized precision, Matthews correlation coefficient, and balanced accuracy score. Experimental results demonstrate that EGADB significantly enhances minority class recognition and overall classification robustness across all evaluated models. The developed EGADB voter ensemble consistently achieves superior performance, attaining near perfect detection metrics on balanced datasets while maintaining stability across evaluation measures. These findings confirm that EGADB provides an effective and scalable solution for mitigating class imbalance and improving ensemble based predictive performance in imbalanced learning environments.

Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, and etc. Numerous previous studies have shown that this type of classifiers achieves high classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, harmony search, and classification based association rules (CBA) algorithm for building a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary harmony search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.

In recent years, the field of multivariate analysis and machine learning evolved rapidly, and provided powerful techniques, which are currently adopted in all fields of science. Prominent use-cases include: image and speech recognition, stock market trading, fraud detection, and medical diagnosis.

Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, etc. Numerous previous studies have shown that this type of classifier achieves a higher classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, Harmony Search, and classification-based association rules (CBA) algorithm in order to build a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary Harmony Search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on a seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.