Search Results - (( features solution mining algorithm ) OR ( data distribution function algorithm ))

A partition based feature selection approach for mixed data clustering / Ashish Dutt by Ashish , Dutt

“…In this thesis, a novel weighted feature selection approach on nominal features is proposed, for a partition. clustering algorithm that can handle mixed data. …”
Get full text
Get full text
Get full text

Tree-based contrast subspace mining method by Florence Sia Fui Sze

“…Genetic algorithm has been widely used to find global solution to optimization and search problem. …”
Get full text
Get full text
Get full text

A multi-layer dimension reduction algorithm for text mining of news in forex / Arman Khadjeh Nassirtoussi by Nassirtoussi, Arman Khadjeh

“…The major finding of this review is that context-specific text mining algorithms are lacking. The main underlying text-mining challenge that seems to deserve immediate attention is the sparse and high dimensional nature of the feature-space. …”
Get full text
Get full text

Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy by Ganesh , Krishnasamy

“…The proposed algorithm is compared with the state-of-the-art feature selection algorithms using three different datasets. …”
Get full text
Get full text
Get full text

Ideal combination feature selection model for classification problem based on bio-inspired approach by Basir, Mohammad Aizat, Hussin, Mohamed Saifullah, Yusof, Yuhanis

“…The important step is to idealize the combined feature selection models by finding the best combination of search method and feature selection algorithms. …”
Get full text
Get full text

Towards a better feature subset selection approach by Shiba, Omar A. A.

“…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
Get full text
Get full text
Get full text

Arabic Text Clustering Methods And Suggested Solutions For Theme-based Quran Clustering: Analysis Of Literature by Qusay Bsoul, Jaffar Atwan, Rosalina Abdul Salam, Malik Jawarneh

Feature and Instances Selection for Nearest Neighbor Classification via Cooperative PSO by Sharifah Sakinah, Syed Ahmad

“…Data reduction is an essential task in the data preparation phase of knowledge discovery and data mining (KDD). The reduction method contains two techniques, namely features reduction and data reduction which are commonly applied to a classification problem. …”
Get full text
Get full text

A hybrid local search algorithm for minimum dominating set problems by Abed, S.A., Rais, H.M., Watada, J., Sabar, N.R.

“…This algorithm focuses on generating promising solutions in different areas of the solution space using the problem search history. …”
Get full text
Get full text

A hybrid local search algorithm for minimum dominating set problems by Abed, S.A., Rais, H.M., Watada, J., Sabar, N.R.

“…This algorithm focuses on generating promising solutions in different areas of the solution space using the problem search history. …”
Get full text
Get full text

A comparative study on ant-colony algorithm and genetic algorithm for mobile robot planning. by Rajendran, Piraviendran, Othman, Muhaini

Get full text
Get full text
Get full text

An Efficient IDS Using Hybrid Magnetic Swarm Optimization in WANETs by Sadiq, Ali Safaa, Alkazemi, Basem, Mirjalili, Seyedali, Ahmed, Noraziah, Khan, Suleman, Ali, Ihsan, Pathan, Al-Sakib Khan, Ghafoor, Kayhan Zrar

“…Our developed algorithm works in extracting the most relevant features that can assist in accurately detecting the network attacks. …”
Get full text
Get full text

An efficient IDS using hybrid Magnetic swarm optimization in WANETs by Sadiq, Ali Safa, Alkazemi, Basem Y., Mirjalili, Seyedali, Noraziah, Ahmad, Khan, Suleman, Ihsan, Ali, Pathan, Al-Sakib Khan, Ghafoor, Kayhan Zrar

“…Our developed algorithm works in extracting the most relevant features that can assist in accurately detecting the network attacks. …”
Get full text
Get full text
Get full text
Get full text

Automatic document clustering and indexing of multiple documents using KNMF for feature extraction through Hadoop and lucene on big data by Laxmi Lydia E., Sharmili N., Nguyen P.T., Hashim W., Maseleno A.

Approaches to Multi-Objective Feature Selection: A Systematic Literature Review by Al-Tashi, Q., Abdulkadir, S.J., Rais, H.M., Mirjalili, S., Alhussian, H.

“…Feature selection has gained much consideration from scholars working in the domain of machine learning and data mining in recent years. …”
Get full text
Get full text

Enhanced Cuckoo Search Algorithm With Metaheuristic Components For Extracting The Maxima Of The Orientation Distribution Function by Shehab, Mohammad Mohammad Said

Get full text
Get full text

Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm by Suparman, S., Rusiman, Mohd Saifullah

“…In the hierarchical Bayesian approach, the order and coefficients of the autoregressive model are assumed to have a prior distribution. The prior distribution is combined with the likelihood function to obtain a posterior distribution. …”
Get full text

Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets by Saeed, Sana

“…Thereafter, a multi-objective hybrid algorithm (MOHA), an extension of the self-adaptive hybrid algorithm is proposed and tested on the established multi-objective (MO) test functions. …”
Get full text
Get full text

Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

“…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
Get full text
Get full text
Get full text

A new Gompertz-three-parameter-lindley distribution for modeling survival time data by Liang, Fei, Lu, Hezhi, Xi, Yuhang

“…The statistical properties of the proposed distribution including the shape properties, cumulative distribution, quantile functions, moment generating function, failure rate function, mean residual function, and stochastic orders are studied. …”
Get full text
Get full text
Get full text