This paper proposes two-stage hybrid feature selection algorithms to build the stable and efficient diagnostic models where a new accuracy measure is introduced to assess the models. The two-stage hybrid algorithms adopt Support Vector Machines (SVM) as a classification tool, and the extended Sequential Forward Search (SFS), Sequential Forward Floating Search (SFFS), and Sequential Backward Flo...

In this paper, we propose a model for automatic classification of Animals using different classifiers Nearest Neighbour, Probabilistic Neural Network and Symbolic. Animal images are segmented using maximal region merging segmentation. The Gabor features are extracted from segmented animal images. Discriminative texture features are then selected using the different feature selection algorithm l...

Multichannel Gabor filters (MGFs) and Markov random fields (MRFs) are two common methods for texture classification. However, the two above methods make the implicit assumption that textures are acquired in the same viewpoint, which is unsuitable for rotation-invariant texture classification. In this paper, rotation-invariant (RI) texture features are developed based on MGF and MRF. A novel alg...

Gene expression studies bridge the gap between DNA information and trait information by dissecting biochemical pathways into intermediate components between genotype and phenotype. These studies open new avenues for identifying complex disease genes and biomarkers for disease diagnosis and for assessing drug efficacy and toxicity. However, the majority of analytical methods applied to gene expr...

This work tackles the problem of selecting a subset of features in an inductive learning setting, by introducing a novel Thermodynamic Feature Selection algorithm (TFS). Given a suitable objective function, the algorithm makes uses of a specially designed form of simulated annealing to find a subset of attributes that maximizes the objective function. The new algorithm is evaluated against one ...

In this paper we suggest feature selection and Principal Component Analysis as a way to analyze and compare corpora of emotional speech. To this end, a fast improvement of the Sequential Forward Floating Search algorithm is introduced, and subsequently extensive tests are run on a selection of French emotional language resources well suited for a first impression on general applicability. Tools...

breast cancer is the most common type of cancer among women. the important key to treat the breast cancer is early detection of it because according to many pathological studies more than 80% of all abnormalities are still benign at primary stages; so in recent years, many studies and extensive research done to early detection of breast cancer with higher precision and accuracy.infra-red breast...

Abstract Aiming at the characteristics of a long short-term memory network (LSTM) which is suitable for processing high-dimensional, strongly coupled, and highly time-dependent data, it combines advantages feature selection to reduce difficulty learning tasks improve performance model fault diagnosis. This paper proposes an LSTM method combining sequential floating forward search with integrate...

In this paper a feature selection algorithm CSSFFS (Constrained search sequential floating forward search) based on SVM is proposed for detecting breast cancer. It is a greedy algorithm with search strategy of constrained search. The aim of this algorithm is to achieve a feature subset with minimal BER (Balanced error rate). This is a hybrid algorithm with the combination of filters and wrapper...

A new boosting algorithm, called FloatBoost, is proposed to overcome the monotonicity problem of the sequential AdaBoost learning. AdaBoost [1, 2] is a sequential forward search procedure using the greedy selection strategy. The premise oÿered by the sequential procedure can be broken-down when the monotonicity assumption, i.e. that when adding a new feature to the current set, the value of the...