Papers by Manomita Chakraborty
Comprehensible and transparent rule extraction using neural network
Multimedia tools and applications, Feb 6, 2024
Explainable Neural Networks: Achieving Interpretability in Neural Models
Archives of computational methods in engineering, Mar 21, 2024
A Review on Rainfall Prediction Using Neural Networks
Computer-Aided Heart Disease Diagnosis Using Recursive Rule Extraction Algorithms from Neural Networks
International Journal of Computational Intelligence and Applications
Mortality rate due to fatal heart disease (HD) or cardiovascular disease (CVD) has increased dras... more Mortality rate due to fatal heart disease (HD) or cardiovascular disease (CVD) has increased drastically over the world in recent decades. HD is a very hazardous problem prevailing among people which is treatable if detected early. But in most of the cases, the disease is not diagnosed until it becomes severe. Hence, it is requisite to develop an effective system which can accurately diagnosis HD and provide a concise description for the underlying causes [risk factors (RFs)] of the disease, so that in future HD can be controlled only by managing the primary RFs. Recently, researchers are using various machine learning algorithms for HD diagnosis, and neural network (NN) is one among them which has attracted tons of people because of its high performance. But the main obstacle with a NN is its black-box nature, i.e., its incapability in explaining the decisions. So, as a solution to this pitfall, the rule extraction algorithms can be very effective as they can extract explainable de...
Health Insurance Cost Prediction Using Regression Models
2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), May 26, 2022
New Generation Computing, Nov 11, 2018
Neural network is one of the best tools for data mining tasks due to its high accuracy. However, ... more Neural network is one of the best tools for data mining tasks due to its high accuracy. However, one of the drawbacks of neural network is its black box nature. This limitation makes neural network useless for many applications which require transparency in their decision-making process. Many algorithms have been proposed to overcome this drawback by extracting transparent rules from neural network, but still researchers are in search for algorithms that can generate more accurate and simple rules. Therefore, this paper proposes a rule extraction algorithm named Eclectic Rule Extraction from Neural Network Recursively (ERENNR), with the aim to generate simple and accurate rules. ERENNR algorithm extracts symbolic classification rules from a single-layer feed-forward neural network. The novelty of this algorithm lies in its procedure of analyzing the nodes of the network. It analyzes a hidden node based on data ranges of input attributes with respect to its output and analyzes an output node using logical combination of the outputs of hidden nodes with respect to output class. And finally it generates a rule set by proceeding in a backward direction starting from the output layer. For each rule in the set, it repeats the whole process of rule extraction if the rule satisfies certain criteria. The algorithm is validated with eleven benchmark datasets. Experimental results show that the generated rules are simple and accurate.
Boston House Price Prediction Using Regression Models
2022 2nd International Conference on Intelligent Technologies (CONIT), Jun 24, 2022
Heart Disease Prediction Using Classification Models
2022 3rd International Conference for Emerging Technology (INCET), May 27, 2022
Early detection of Parkinson disease using stacking ensemble method
Computer Methods in Biomechanics and Biomedical Engineering, May 19, 2022
SN computer science, Apr 23, 2021
Nowadays, people are facing various health-related problems due to the modern life style what the... more Nowadays, people are facing various health-related problems due to the modern life style what they follow. Breast Cancer is one of the most common problems among women worldwide which affects approximately 2.1 million women each year. Hence, it has become paramount to develop a system that can identify the major risk factors of Breast Cancer beforehand to make women aware about the risk factors and to take some precautionary measures to manage Breast Cancer. Consequently, this paper proposes a system called Transparent Breast Cancer Management System using P-Rules (TBCMS-PR) which identifies the major risk factors responsible for Breast Cancer in detail using decision tree and neural Network. TBCMS-PR uses decision tree to generate the rules for deciding the decision of Breast Cancer. Neural Network is used to keep only the relevant rules for Breast Cancer and to drop the irrelevant ones. Finally, the major risk factors with ranges are identified based on Sequential Search algorithm. The performance of the TBCMS-PR system is validated with the Breast Cancer dataset collected from UCI repository and is compared with a recent existing system. From the experimental results, it is observed that the proposed TBCMS-PR is significant and potential to manage Breast Cancer to a great extent by managing only one or two major risk factor(s).
Computer-Aided Heart Disease Diagnosis Using Recursive Rule Extraction Algorithms from Neural Networks
International Journal of Computational Intelligence and Applications, Jun 1, 2022
Mortality rate due to fatal heart disease (HD) or cardiovascular disease (CVD) has increased dras... more Mortality rate due to fatal heart disease (HD) or cardiovascular disease (CVD) has increased drastically over the world in recent decades. HD is a very hazardous problem prevailing among people which is treatable if detected early. But in most of the cases, the disease is not diagnosed until it becomes severe. Hence, it is requisite to develop an effective system which can accurately diagnosis HD and provide a concise description for the underlying causes [risk factors (RFs)] of the disease, so that in future HD can be controlled only by managing the primary RFs. Recently, researchers are using various machine learning algorithms for HD diagnosis, and neural network (NN) is one among them which has attracted tons of people because of its high performance. But the main obstacle with a NN is its black-box nature, i.e., its incapability in explaining the decisions. So, as a solution to this pitfall, the rule extraction algorithms can be very effective as they can extract explainable decision rules from NNs with high prediction accuracies. Many neural-based rule extraction algorithms have been applied successfully in various medical diagnosis problems. This study assesses the performance of rule extraction algorithms for HD diagnosis, particularly those that construct rules recursively from NNs. Because they subdivide a rule’s subspace until the accuracy improves, recursive algorithms are known for delivering interpretable decisions with high accuracy. The recursive rule extraction algorithms’ efficacy in HD diagnosis is demonstrated by the results. Along with the significant data ranges for the primary RFs, a maximum accuracy of 82.59% is attained.
An Improvised Particle Swarm Optimization Using Balanced Local Best
Springer eBooks, 2021
International Journal of Bio-inspired Computation, 2016
Classification is one of the important tasks of data mining and neural network is one of the best... more Classification is one of the important tasks of data mining and neural network is one of the best known tools for doing this task. Despite of producing high classification accuracy, the black box nature of neural network makes it useless for many applications which require transparency in its decision-making process. This drawback is overcome by extracting rules from neural network. Rule extraction makes neural network an alternative to other machine learning methods for handling classification problems by deriving an explanation of how each decision is made. Till now, many algorithms on rule extraction have been proposed but still research on this area is going on to find out more accurate and understandable rules. The proposed algorithm extracts rules from trained neural network for datasets with mixed mode attributes using pedagogical approach. The proposed algorithm uses classified and misclassified patterns to find out the data ranges of significant attributes in respective classes. The experimental results clearly show that the proposed algorithm produces accurate and understandable rules compared to existing algorithms.
An ANN based Classification Algorithm for Swine Flu Diagnosis
International journal of knowledge based computer systems, 2015
Machine learning technology adds a new potential to medical diagnosis systems. This paper present... more Machine learning technology adds a new potential to medical diagnosis systems. This paper presents an Artificial Neural Network (ANN) based swine flu diagnosis model. The proposed model selects significant features for swine flu diagnosis by a feature selection algorithm using k- Nearest Neighbour (k-NN) classifier, which reduces the size of data to be used for training the ANN model with an objective of making the training more efficient and accurate. A threshold value is determined by ANN to identify positive and negative cases and the model classifies the test cases either positive or negative based on the threshold value. The results obtained with the proposed model demonstrate the ability of the model to provide high level of accuracy for swine flu diagnosis. The assessment (classification) ability of the proposed ANN based model is compared with that of Case Based Reasoning (CBR) approaches and is observed that the proposed model is superior to others.
A Medical Expert System to Identify Major Factor of Diseases Using P-Rules
Nowadays, people face various health related problems due to the busy Iifestyle and hectic work p... more Nowadays, people face various health related problems due to the busy Iifestyle and hectic work pressure. Under this circumstance, if it is possible to have a system that identifies the major causes of health related problems then people can follow some precautionary measures to prevent those causes and can have disease free life. This paper proposes an algorithm, called Rule Based Major Feature Identification (RBMFI) which identifies major feature of medical problems by pruning p-rules generated by decision tree. RBMFI algorithm comprises of three phases: Rule generation, Rule pruning and Attribute pruning. Rule generation generates the rules using decision tree, rule pruning removes the rules that do not affect the overall classification accuracy and attribute pruning keeps only the major feature of a decision. Finally, the major feature is treated as major cause of a decision (disease). The proposed method is validated with three medical datasets taken from UCI repository. It is observed from the experimental results that the proposed RBMFI can identify the major feature with good accuracy.
International journal of big data intelligence, 2016
Many models have been developed for rainfall forecasting from time to time. Artificial neural net... more Many models have been developed for rainfall forecasting from time to time. Artificial neural networks (ANNs) using back propagation algorithm, are the most popular and widely used forecasting models in the present decade. Rainfall depends on many weather parameters (attributes) including pressure, temperature, wind speed, and so on. A set of attributes is habitually used for rainfall prediction, which consists of relevant and irrelevant attributes and from the viewpoint of managing a dataset which can be enormous. Hence, reducing the number of attributes by selecting only the relevant ones is desirable. Doing so, higher performance with lower computational effort is expected. Therefore, feature selection needs to be done to identify the effective parameters to improve the forecasting ability of a model. Consequently, this paper proposes a feature selection algorithm for rainfall forecasting using neural network and investigates the performance of different ANN methods such as multi-layer feed forward neural network (MLFNN), radial basis function neural network (RBFNN), focused time delay neural network (FTDNN) and nonlinear autoregressive exogenous input neural network (NARXNN). From the empirical results, it is observed that NARXNN produces better predicted accuracy than others. It is also observed that the proposed model outperforms one earlier forecasting model.
Knowledge and Information Systems, May 6, 2020
Representing the knowledge learned by neural networks in the form of interpretable rules is a pru... more Representing the knowledge learned by neural networks in the form of interpretable rules is a prudent technique to justify the decisions made by neural networks. Heretofore many algorithms exist to extract symbolic rules from neural networks, but among them, a few extract rules from deep neural networks trained using deep learning techniques. So, this paper proposes an algorithm to extract rules from a multi-hidden layer neural network, pre-trained using deep belief network and fine-tuned using back propagation. The algorithm analyzes each node of a layer and extracts knowledge from each layer separately. The process of knowledge extraction from the first hidden layer is different from the other layers. Consecutively, the algorithm combines all the knowledge extracted and refines them to construct a final ruleset consisting of symbolic rules. The algorithm further subdivides the subspace of a rule in the ruleset if it satisfies certain conditions. Results show that the algorithm extracted rules with higher accuracy compared to some existing rule extraction algorithms. Other than accuracy, the efficacy of the extracted rules is also validated with fidelity and various other performance measures.
Soft Computing, Aug 18, 2016
Case-based reasoning (CBR) is an artificial intelligent approach to learning and problem-solving,... more Case-based reasoning (CBR) is an artificial intelligent approach to learning and problem-solving, which solves a target problem by relating past similar solved problems. But it faces the challenge of weights assignment to features to measure similarity between cases. There are many methods to overcome this feature weighting problem of CBR. However, neural network's pruning is one of the powerful and useful methods to overcome this feature weighting problem, which extracts feature weights from trained neural network without losing the generality of training set by four popular mechanisms: sensitivity, activity, saliency and relevance. It is habitually assumed that the training sets used for learning are balanced. However, this hypothesis is not always true in real-world applications, and hence, the tendency is to yield classification models that are biased toward the overrepresented class. Therefore, a hybrid CBR system is proposed in this paper to overcome this problem, which adopts a cost-sensitive back-propagation neural network (BPNN) in network pruning to find feature weights. These weights are used in CBR. A single cost parameter is used by the costsensitive BPNN to distinguish the importance of class errors. A balanced decision boundary is generated by the cost parameter using prior information. Thus, the class imbalance problem of network pruning is overcome to improve the accuracy of the hybrid CBR. From the empirical results, it is observed that the performance of the proposed hybrid CBR Communicated by V. Loia.
Rule Extraction from Training Data Using Neural Network
International Journal on Artificial Intelligence Tools, Dec 22, 2016
Data Mining is a powerful technology to help organization to concentrate on most important data b... more Data Mining is a powerful technology to help organization to concentrate on most important data by extracting useful information from large database. One of the most commonly used techniques in data mining is Artificial Neural Network due to its high performance in many application domains. Despite many advantages of Artificial Neural Network, one of its main drawbacks is its inherent black box nature which is the main problem of using Artificial Neural Network in data mining. Therefore, this paper proposes a rule extraction algorithm from neural network using classified and misclassified data to convert the black box nature of Artificial Neural Network into a white box. The proposed algorithm is a modification of the existing algorithm, Rule Extraction by Reverse Engineering (RxREN). The proposed algorithm extracts rules from trained neural network for datasets with mixed mode attributes using pedagogical approach. The proposed algorithm uses both classified as well as misclassified data to find out the data ranges of significant attributes in respective classes, which is the innovation of the proposed algorithm. The experimental results clearly show that the performance of the proposed algorithm is superior to existing algorithms.
Journal of Organizational and End User Computing, Oct 1, 2018
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Papers by Manomita Chakraborty