| 000 | 03403nlm1a2200529 4500 | ||
|---|---|---|---|
| 001 | 666030 | ||
| 005 | 20231030042023.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\37234 | ||
| 035 | _aRU\TPU\network\37231 | ||
| 090 | _a666030 | ||
| 100 | _a20211130a2021 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 135 | _adrgn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aBoosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection _fMohamed Abd Elaziz, A. Laith, Yo. Dalia [et al.] |
|
| 203 |
_aText _celectronic |
||
| 300 | _aTitle screen | ||
| 320 | _a[References: 57 tit.] | ||
| 330 | _aFeature selection (FS) is a well-known preprocess step in soft computing and machine learning algorithms. It plays a critical role in different real-world applications since it aims to determine the relevant features and remove other ones. This process (i.e., FS) reduces the time and space complexity of the learning technique used to handle the collected data. The feature selection methods based on metaheuristic (MH) techniques established their performance over all the conventional FS methods. So, in this paper, we presented a modified version of new MH techniques named Atomic Orbital Search (AOS) as FS technique. This is performed using the advances of dynamic opposite-based learning (DOL) strategy that is used to enhance the ability of AOS to explore the search domain. This is performed by increasing the diversity of the solutions during the searching process and updating the search domain. A set of eighteen datasets has been used to evaluate the efficiency of the developed FS approach, named AOSD, and the results of AOSD are compared with other MH methods. From the results, AOSD can reduce the number of features by preserving or increasing the classification accuracy better than other MH techniques. | ||
| 461 | _tMathematics | ||
| 463 |
_tVol. 9, iss. 21 _v[2786, 17 p.] _d2021 |
||
| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _asoft computing | |
| 610 | 1 | _amachine learning | |
| 610 | 1 | _afeature selection (FS) | |
| 610 | 1 | _ametaheuristic (MH) | |
| 610 | 1 | _aatomic orbital search (AOS) | |
| 610 | 1 | _adynamic opposite-based learning (DOL) | |
| 610 | 1 | _aвычисления | |
| 610 | 1 | _aмашинное обучение | |
| 610 | 1 | _aметаэвристика | |
| 610 | 1 | _aатомно-орбитальные модели | |
| 701 | 0 | _aMohamed Abd Elaziz | |
| 701 | 1 |
_aLaith _bA. _gAbualigah |
|
| 701 | 1 |
_aDalia _bYo. _gYousri |
|
| 701 | 1 |
_aOliva Navarro _bD. A. _cspecialist in the field of informatics and computer technology _cProfessor of Tomsk Polytechnic University _f1983- _gDiego Alberto _2stltpush _3(RuTPU)RU\TPU\pers\37366 |
|
| 701 | 0 | _aMohammed A. A. Al-Qaness | |
| 701 | 0 | _aMohammad H. Nadimi-Shahraki | |
| 701 | 1 |
_aEwees _bA. A. _gAhmed |
|
| 701 | 1 |
_aSongfeng _bL. _gLu |
|
| 701 | 1 |
_aRehab _bA. I. _gAli Ibrahim |
|
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет _bИнженерная школа информационных технологий и робототехники _bОтделение информационных технологий _h7951 _2stltpush _3(RuTPU)RU\TPU\col\23515 |
| 801 | 2 |
_aRU _b63413507 _c20211130 _gRCR |
|
| 856 | 4 | _uhttps://doi.org/10.3390/math9212786 | |
| 942 | _cCF | ||