Selection of attributes has been the current emerging research area for a long while and much work has been finished on. With the making of tremendous dataset and the resulting necessities for good machine learning systems, new issues emerge and ways to deal with feature selection are the area of research and in need. Preprocessed and generated datasets of multimedia event detection with numerical values is the input for our research. This paper recognizes the contribution of each attribute and combinational attributes towards the accuracy of the classifier model. The analysis is done using LIBSVM tool and different kernel modes using Support Vector Machine (SVM) classifier. After the analysis is done, we will get to know that which attribute yields the maximum accuracy in classifying the instances. © Published under licence by IOP Publishing Ltd.

Dharmendra Singh Rajput

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Praveen Kumar Reddy Maddikunta

Department of Information Technology

Thippa Reddy Gadekallu

Singh A.P

Agarwal Y

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Feature selection analysis for multimedia event detection

IOP Conference Series: Materials Science and Engineering

Disease forecasting using soft computing techniques is major area of research in data mining in recent years. To classify heart and diabetes diseases, this paper proposes a diagnosis system using cuckoo search optimized rough sets based attribute reduction and fuzzy logic system. The disease prediction is done as per the following steps 1) feature reduction using cuckoo search with rough set theory 2) Disease prediction using fuzzy logic system. The first step reduces the computational burden and enhances performance of fuzzy logic system. Second step is based on the fuzzy rules and membership functions which classifies the disease datasets. The authors have tested this approach on Cleveland, Hungarian, Switzerland heart disease data sets and a real-time diabetes dataset. The experimentation result demonstrates that the proposed algorithm outperforms the existing approaches.

International Journal of Fuzzy System Applications

Cuckoo Search Optimized Reduction and Fuzzy Logic Classifier for Heart Disease and Diabetes Prediction

The objective of the work is to predict heart disease using computing techniques like an oppositional firefly with BAT and rule-based fuzzy logic (RBFL). The system would help the doctors to automate heart disease diagnosis and to enhance the medical care. In this paper, a hybrid OFBAT-RBFL heart disease diagnosis system is designed. Here, at first, the relevant features are selected from the dataset using locality preserving projection (LPP) algorithm which helps the diagnosis system to develop a classification model using the fuzzy logic system. After that, the rules for the fuzzy system are created from the sample data. Among the entire rules, the important and relevant group of rules are selected using OFBAT algorithm. Here, the opposition based learning (OBL) is hybrid to the firefly with BAT algorithm to improve the performance of the FAT algorithm while optimizing the rules of the fuzzy logic system. Next, the fuzzy system is designed with the help of designed fuzzy rules and membership functions so that classification can be carried out within the fuzzy system designed. At last, the experimentation is performed by means of publicly available UCI datasets, i.e., Cleveland, Hungarian and Switzerland datasets. The experimentation result proves that the RBFL prediction algorithm outperformed the existing approach by attaining the accuracy of 78%.

Journal of Circuits, Systems and Computers

An Efficient System for Heart Disease Prediction Using Hybrid OFBAT with Rule-Based Fuzzy Logic Model

International Journal of Recent Trends in Engineering and Research

An Enhanced Approach for Privacy Preserving Data Mining (PPDM)

International Journal of Science and Research (IJSR)

A Novel Way for Mining Frequent and Interesting Patterns using Genetic Algorithm

Journal	Data powered by TypesetIOP Conference Series: Materials Science and Engineering
Publisher	Data powered by TypesetIOP Publishing
ISSN	1757-8981
Open Access	Yes