Feature selection (FS) is an important task which can significantly affect the performance of image classification and recognition. In this paper, we present a feature selection algorithm based on ant colony optimization (ACO). For n features, existing ACO-based feature selection methods need to traverse a complete graph with O(n) edges. However, we propose a novel algorithm in which the artifi...

Feature selection has improved the performance of text clustering. In this paper, a local feature selection technique is incorporated in the dynamic hierarchical compact clustering algorithm to speed up the computation of similarities. We also present a quality measure to evaluate hierarchical clustering that considers the cost of finding the optimal cluster from the root. The experimental resu...

The problem of automatic classification of seven types of eating conditions from speech is considered. Based on the confusion among different eating conditions from a seven class support vector machine (SVM) classifier, a hierarchical SVM classifier is designed. Experiments on the iHEARu-EAT database show that the hierarchical classifier results in a better classification accuracy compared to a...

Received: KK Revised: KK Accepted: KK Abstract Unknown (and unexpected) multivariate patterns lurking in high-dimensional datasets are often very hard to find. This paper describes a human-centered exploration environment, which incorporates a coordinated suite of computational and visualization methods to explore high-dimensional data for uncovering patterns in multivariate spaces. Specificall...

Data mining (DM) involves the process of identifying patterns, correlation, and anomalies existing in massive datasets. The applicability DM includes several areas such as education, healthcare, business, finance. Educational Mining (EDM) is an interdisciplinary domain which focuses on DM, machine learning (ML), statistical approaches for pattern recognition quantities educational data. This ty...

Image feature selection (FS) is an important task which can affect the performance of image classification and recognition. In this paper, we present a feature selection algorithm based on ant colony optimization (ACO). For n features, most ACO-based feature selection methods use a complete graph with O(n) edges. However, the artificial ants in the proposed algorithm traverse on a digraph with ...

Feature Selection (FS) is a crucial preprocessing step in Text Classification (TC) systems. FS can be either Class-Based or Corpus-Based. Polynomial Network (PN) classifiers have proved recently to be competitive in TC using a very small subset of corpora features. This paper presents an empirical study of the performance of PN classifiers using Aggressive Class-Based FS. Seven of the stateof-t...

Objective(s): This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. Materials and Methods: To evaluate effectiveness of proposed feature selection method, we ...

A Classifier Ensemble (CE) efficiently improves the generalization ability of the classifier compared to a single classifier. This paper proposes an alternate approach for Integration of classifier ensembles. Initially three classifiers that are highly diverse and showed good classification accuracy when applied to six UCI (University of California, Irvine) datasets are selected. Then Feature S...

With the recent financial crisis and European debt crisis, corporate bankruptcy prediction has become an increasingly important issue for financial institutions. Many statistical and intelligent methods have been proposed, however, there is no overall best method has been used in predicting corporate bankruptcy. Recent studies suggest ensemble learning methods may have potential applicability i...